From 6afc1cd0e53f9704be605c8bedbf0d1faea9eaa8 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Wed, 27 Jul 2022 09:18:31 -0300 Subject: [PATCH 01/21] first 4 update items --- R/kin.R | 63 +++++++------ R/{kin_stable.R => kin_time_invariant.R} | 36 ++++---- R/{kin_non_stable.R => kin_time_variant.R} | 102 +++++++++++---------- tests/repl_caswell.R | 30 +++++- 4 files changed, 136 insertions(+), 95 deletions(-) rename R/{kin_stable.R => kin_time_invariant.R} (74%) rename R/{kin_non_stable.R => kin_time_variant.R} (66%) diff --git a/R/kin.R b/R/kin.R index 5511c24..bf69ea0 100644 --- a/R/kin.R +++ b/R/kin.R @@ -2,17 +2,16 @@ #' @description Implementation of Goodman-Keyfitz-Pullum equations in a stable or not framework. #' @details See Caswell (2019) and Caswell (2021) for details on formulas. -#' @param U numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year. -#' @param f numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year. -#' @param N numeric. A matrix of population with rows as ages and columns as years. The name of each col must be the year. -#' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year. -#' @param focal_cohort integer. Year of birth of focal. Could be a vector. Should be within input data years range. -#' @param focal_year integer. Year of focal. Could be a vector. Should be within input data years range. -#' @param selected_kin character. kin to return: "m" for mother, "d" for daughter,... +#' @param U numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). +#' @param f numeric. Same as U but for fertility rates. +#' @param time_invariant logical. Constant assumption for a given `year` rates. +#' @param N numeric. Same as U but for population distribution (counts or `%`). Optional. +#' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional. +#' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range. +#' @param output_period integer. Vector of period years for returning results. Should be within input data years range. +#' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,... #' @param birth_female numeric. Female portion at birth. -#' @param Pb logic. Is given Pb as the first row in P?. If not, takes `P(0,1)` as `P(b,1)`. Useful for fertility matrix first row. Default `FALSE`. -#' @param stable logical. Stable assumption given `year` rates, taking focal_year in case is U and f are matrix. -#' @param living logial. Only living kin counts `TRUE`, or including death kin `FALSE`. +#' @param living logical. Only living kin counts `TRUE`, or including death kin `FALSE`. #' @return A list with: #' * `kin`: a data frame with focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.). The content (living or deaths) dependes on `alive` param. #' * A data frame with kin at each age of focal´s life. The content (living or deaths) dependes on `alive` param. @@ -35,33 +34,43 @@ #' } #' # get kin ---------------------------------------------------------------- -kin <- function(U = NULL, f = NULL, N = NULL, pi = NULL, - stable = TRUE, - focal_cohort = NULL, focal_year = NULL, selected_kin=NULL, +kin <- function(U = NULL, f = NULL, + time_invariant = TRUE, + N = NULL, pi = NULL, + output_cohort = NULL, output_period = NULL, output_kin=NULL, birth_female = 1/2.04, - Pb = FALSE, living = TRUE) { - age = as.integer(rownames(U)) - years_data = as.integer(colnames(U)) + age <- as.integer(rownames(U)) + years_data <- as.integer(colnames(U)) - # if stable or not - if(stable){ + # kin to return + all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") + if(is.null(output_kin)){ + output_kin <- all_possible_kin + }else{ + output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE) + } + + # if time dependent or not + if(time_invariant){ if(!is.vector(U)) { - U = U[,as.character(focal_year)] - f = f[,as.character(focal_year)] + focal_year <- min(years_data) + U <- U[,as.character(focal_year)] + f <- f[,as.character(focal_year)] } - kin_full <- kin_stable(U = U, f = f, selected_kin=selected_kin, birth_female = birth_female) %>% - mutate(cohort = 0, year = 0) + kin_full <- kin_time_invariant(U = U, f = f, + output_kin = output_kin, birth_female = birth_female) %>% + mutate(cohort = NA, year = NA) }else{ - kin_full <- kin_non_stable(U = U, f = f, N = N, pi = pi, - focal_cohort = focal_cohort, focal_year = focal_year, - selected_kin=selected_kin, - birth_female = birth_female, - Pb = Pb) + kin_full <- kin_time_variant(U = U, f = f, N = N, pi = pi, + output_cohort = output_cohort, output_period = output_period, + output_kin = output_kin, + birth_female = birth_female) message(paste0("Assuming stable population before ", min(years_data), ".")) } + # reorder kin_full <- kin_full %>% select(year, cohort, age_focal, kin, age_kin, alive, count) diff --git a/R/kin_stable.R b/R/kin_time_invariant.R similarity index 74% rename from R/kin_stable.R rename to R/kin_time_invariant.R index 8da0988..ca1c305 100644 --- a/R/kin_stable.R +++ b/R/kin_time_invariant.R @@ -1,23 +1,23 @@ -#' Estimate kin counts in a stable framework +#' Estimate kin counts in a time invariant framework #' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). -#' @param U numeric. A vector of survival ratios. -#' @param f numeric. A vector of age-specific fertility rates. +#' @param U numeric. A vector of survival ratios with same length as ages. +#' @param f numeric. A vector of age-specific fertility rates with same length as ages. #' @param birth_female numeric. Female portion at birth. -#' @param pi numeric. For using the non-stable distribution of childbearing. Default `NULL`. -#' @param selected_kin character. kin to return: "m" for mother, "d" for daughter,... +#' @param pi numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default `NULL`. +#' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. #' @param pi_stable logical. Want mean age at childbearing as a result too. Default `FALSE` -#' @param list_output logical. Want kin results as a list. Default `FALSE` +#' @param list_output logical. Results as a list. Default `FALSE` #' #' @return A data frame with focal´s age, related ages and type of kin #' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. #' @export -kin_stable <- function(U = NULL, f = NULL, +kin_time_invariant <- function(U = NULL, f = NULL, birth_female = 1/2.04, pi = NULL, - selected_kin = NULL, + output_kin = NULL, pi_stable = FALSE, list_output = FALSE){ @@ -31,9 +31,7 @@ kin_stable <- function(U = NULL, f = NULL, Ut = rbind(cbind(Ut,zeros), cbind(Mt,Dcum)) ft = matrix(0, nrow=ages*2, ncol=ages*2) - - # Caswell's assumption - ft[1,1:ages] = f * U * birth_female + ft[1,1:ages] = f * birth_female # stable age distr if(is.null(pi)){ @@ -44,13 +42,14 @@ kin_stable <- function(U = NULL, f = NULL, pi = w*A[1,]/sum(w*A[1,]) } - # identity + # identity matrix e = matrix(0, ages * 2, ages * 2) diag(e[1:ages,1:ages]) = 1 # initial vectors d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, ages * 2, ages) + # focal´s trip m[,1] = c(pi, rep(0,ages)) for(i in 1:(ages-1)){ d[,i+1] = Ut %*% d[,i] + ft %*% e[,i] @@ -94,30 +93,33 @@ kin_stable <- function(U = NULL, f = NULL, nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya) # only selected kin - if(!is.null(selected_kin)){ - kin_list <- kin_list %>% keep(names(.) %in% selected_kin) + if(!is.null(output_kin)){ + kin_list <- kin_list %>% keep(names(.) %in% output_kin) } + # as data.frame kin <- map2(kin_list, names(kin_list), function(x,y){ - out = as.data.frame(x) - colnames(out) = age + out <- as.data.frame(x) + colnames(out) <- age out %>% mutate(kin = y, age_kin = rep(age,2), alive = c(rep("yes",ages), rep("no",ages))) %>% - gather(age_focal,count,-age_kin, -kin, -alive) %>% + pivot_longer(c(-age_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% mutate(age_focal = as.integer(age_focal)) } ) %>% reduce(rbind) + # add stable results? if(pi_stable){ out <- list(kin=kin, pi_stable=pi) }else{ out <- kin } + # results as list? if(list_output) out <- kin_list return(out) diff --git a/R/kin_non_stable.R b/R/kin_time_variant.R similarity index 66% rename from R/kin_non_stable.R rename to R/kin_time_variant.R index 50cb58c..e92e7b2 100644 --- a/R/kin_non_stable.R +++ b/R/kin_time_variant.R @@ -1,57 +1,40 @@ -#' Estimate kin counts in a non stable framework +#' Estimate kin counts in a time variant framework -#' @description Implementation of non-stable Goodman-Keyfitz-Pullum equations based on Caswell (2021). +#' @description Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021). #' -#' @param U numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year. -#' @param f numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year. -#' @param N numeric. A matrix of population with rows as ages and columns as years. The name of each col must be the year. -#' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year. -#' @param focal_cohort integer. Year of birth of focal. Could be a vector. Should be within input data years range. -#' @param focal_year integer. Year of focal. Could be a vector. Should be within input data years range. -#' @param selected_kin character. kin to return: "m" for mother, "d" for daughter,... +#' @param U numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval. +#' @param f numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. Coincident with `U`. +#' @param N numeric. A matrix of population with rows as ages and columns as years. Coincident with `U`. +#' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with `U`. +#' @param output_cohort integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range. +#' @param output_period integer. Year of focal to return as output. Could be a vector. Should be within input data years range. +#' @param output_kin character. kin to return as output: "m" for mother, "d" for daughter,... See `vignette` for exahustive kin. #' @param birth_female numeric. Female portion at birth. -#' @param Pb logic. Is given Pb as the first row in P?. If not, takes `P(0,1)` as `P(b,1)`. Useful for fertility matrix first row. Default `FALSE`. #' #' @return A data frame with focal´s age, related ages and type of kin #' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. #' @export -kin_non_stable <- function(U = NULL, f = NULL, N = NULL, pi = NULL, - focal_cohort = NULL, focal_year = NULL, selected_kin = NULL, - birth_female = 1/2.04, - Pb = FALSE){ +kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, + output_cohort = NULL, output_period = NULL, output_kin = NULL, + birth_female = 1/2.04){ # check input - stopifnot(!is.null(U)&!is.null(f)&any(!is.null(N)|!is.null(pi))) + if(is.null(U) | is.null(f)) stop("You need values on U and/or f.") # diff years - if(!any(as.integer(colnames(U))==as.integer(colnames(f))))stop("Data should be from same years.") + if(!any(as.integer(colnames(U)) == as.integer(colnames(f)))) stop("Data should be from same years.") - # data should be from consequtive years - years_data = as.integer(colnames(U)) - if(any(diff(years_data)!=1))stop("Data should be for consecutive years.") + # data should be from same interval years + years_data <- as.integer(colnames(U)) + if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again") - # half year and half age + # utils age <- 0:(nrow(U)-1) n_years_data <- length(years_data) ages <- length(age) om <- max(age) zeros <- matrix(0, nrow=ages, ncol=ages) - if(Pb){ - stopifnot(length(years_data)==ncol(Pb)) - }else{ - Pb = U[1,] - } - - # age distribution at childborn - if(is.null(pi)){ - if(is.null(N)){ - stop("You need data on pi or N.") - }else{ - pi <- rbind(t(t(N * f)/colSums(N * f)), - matrix(0,ages,length(years_data))) - } - } # get lists of matrix Ul = fl = list() @@ -62,46 +45,69 @@ kin_non_stable <- function(U = NULL, f = NULL, N = NULL, pi = NULL, diag(Mt) = 1 - U[,t] Ul[[as.character(years_data[t])]] <- rbind(cbind(Ut,zeros),cbind(Mt,Dcum)) ft = matrix(0, nrow=ages*2, ncol=ages*2) - ft[1,1:ages] = f[,t] * birth_female * (1+U[,t])/2 * as.numeric(Pb[t]) + ft[1,1:ages] = f[,t] * birth_female fl[[as.character(years_data[t])]] <- ft } U <- Ul f <- fl + # age distribution at childborn + if(is.null(pi)){ + if(is.null(N)){ + # create pi and fill it during the loop + message("Stable assumption was made for calculating pi on each year because no input data.") + pi <- matrix(0, nrow=ages, ncol=n_years_data) + }else{ + pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data))) + } + } + # loop over years (more performance here) kin_all <- list() + pb <- progress_bar$new( + format = "Running over input years [:bar] :percent", + total = n_years_data, clear = FALSE, width = 60) for (iyear in 1:n_years_data){ # print(iyear) - Ut <- as.matrix(U[[iyear]]); - ft <- as.matrix(f[[iyear]]); - pit <- pi[,iyear]; + Ut <- as.matrix(U[[iyear]]) + ft <- as.matrix(f[[iyear]]) + if(is.null(pi)){ + A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages] + A_decomp = eigen(A) + w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1])) + pit <- pi[,iyear] <- w*A[1,]/sum(w*A[1,]) + }else{ + pit <- pi[,iyear] + } if (iyear==1){ U1 <- c(diag(Ut[-1,])[1:om],Ut[om,om]) f1 <- ft[1,][1:ages] pi1 <- pit[1:ages] - kin_all[[1]] <- kin_stable(U = U1, f = f1, pi = pi1, birth_female = birth_female, list_output = TRUE) + kin_all[[1]] <- kin_time_invariant(U = U1, f = f1, pi = pi1, birth_female = birth_female, list_output = TRUE) } kin_all[[iyear+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[iyear]]) + pb$tick() } # filter years and kin that were selected names(kin_all) <- as.character(years_data) - if(!is.null(focal_cohort)){ - selected_cohorts_year_age <- data.frame(age = rep(age,length(focal_cohort)), - year = map(focal_cohort,.f = ~.x+age) %>% + if(!is.null(output_cohort)){ + selected_cohorts_year_age <- data.frame(age = rep(age,length(output_cohort)), + year = map(output_cohort,.f = ~.x+age) %>% unlist(use.names = F)) }else{selected_cohorts_year_age <- c()} - if(!is.null(focal_year)){selected_years_age <- expand.grid(age, focal_year) %>% rename(age=1,year=2) + if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% rename(age=1,year=2) }else{selected_years_age <- c()} - if(is.null(focal_year) & is.null(focal_cohort)){ - focal_year = years_data + if(is.null(output_period) & is.null(output_cohort)){ + message("No specific output was set. Return all period data.") + output_period <- years_data } out_selected <- bind_rows(selected_years_age,selected_cohorts_year_age) %>% distinct() possible_kin <- c("d","gd","ggd","m","gm","ggm","os","ys","nos","nys","oa","ya","coa","cya") - if(is.null(selected_kin)){ + if(is.null(output_kin)){ selected_kin_position <- 1:length(possible_kin) }else{ - selected_kin_position <- which(possible_kin %in% selected_kin) + selected_kin_position <- which(possible_kin %in% output_kin) } # first filter diff --git a/tests/repl_caswell.R b/tests/repl_caswell.R index b428016..9fa372c 100644 --- a/tests/repl_caswell.R +++ b/tests/repl_caswell.R @@ -1,13 +1,37 @@ # replicating Caswell´s figures: choose some kin +library(devtools) +load_all() library(DemoKin) +library(tidyverse) + +#time invariant + +# Caswell's assumption on time invariant !!!!!!!!!!!!!!!!!!!!!! +ft[1,1:ages] = f * U * birth_female + +swe_surv_2010 <- swe_surv %>% pull(`2011`) +swe_asfr_2010 <- swe_asfr %>% pull(`2011`) +debugonce(kin) +swe50_2015_stable <- kin(U = swe_surv_2010, f = swe_asfr_2010, output_cohort = c(1911,1930), + output_kin = c("d","m")) + +swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, time_invariant = F, + birth_female = 1, + output_cohort = c(1911), + output_kin = c("d")) + +U = U_caswell_2021; f = f_caswell_2021; pi = pi_caswell_2021; birth_female = 1; +output_cohort = c(1911);output_period = NULL; output_kin = c("d") # FIGURE 5 # cohort -swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, pi = pi_caswell_2021, stable = F,birth_female = 1, - focal_cohort = c(1891,1911,1931,1951,1971,2011,2041), - selected_kin = c("d","gd","ggd","m","gm","ggm")) +swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, pi = pi_caswell_2021, time_invariant = F, + birth_female = 1, + output_cohort = c(1891,1911,1931,1951,1971,2011,2041), + output_kin = c("d","gd","ggd","m","gm","ggm")) + swe_kin_cohorts$kin_summary %>% ggplot(aes(year,count,color=factor(cohort))) + scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ From 83458efb5d3870782a6f7669568dd04b87524813 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Mon, 8 Aug 2022 09:25:16 -0300 Subject: [PATCH 02/21] ignore matlab files --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index ffd982f..b70a570 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,6 @@ .Ruserdata inst/doc diego.txt +.m +.mat +.csv From 992c25d6b5c8f311d4626c6529ce13e00a105954 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Mon, 8 Aug 2022 09:27:34 -0300 Subject: [PATCH 03/21] updates --- R/kin.R | 13 - R/kin_time_invariant.R | 21 +- R/kin_time_variant.R | 27 +- tests/repl_caswell.R | 423 +++++++++++++++++++++++++++++--- tests/repl_caswell_first_year.R | 106 ++++++++ vignettes/Use.Rmd | 69 +++--- 6 files changed, 567 insertions(+), 92 deletions(-) create mode 100644 tests/repl_caswell_first_year.R diff --git a/R/kin.R b/R/kin.R index bf69ea0..70e1497 100644 --- a/R/kin.R +++ b/R/kin.R @@ -19,19 +19,6 @@ #' * Mean age of each type of kin at actual age of focal. The content (living or deaths) dependes on `alive` param. #' * Total of each type of kin at actual age of focal. The content (living or deaths) dependes on `alive` param. #' @export -#' @examples -#' # If focal is 30 years old and lives in 1950. How much live kin would have if -#' # her relatives would experience mortality and fertility in that calendar year? -#' \dontrun{ -#' swe30_1950_stable <- kin(U = swe_surv, f = swe_asfr, focal_year = 1950, stable = TRUE,selected_kin = c("m","gm")) -#' # How much live mothers and grandmothers would have if her relatives would experience -#' # mortality and fertility at each observed year? -#' swe30_1950_nonstable <- kin(U = swe_surv, f = swe_asfr,N = swe_pop, -#' stable = FALSE, focal_year = 1950, selected_kin = c("m","gm")) -#' # Difference in total by kin: -#' swe30_1950_stable$kin_by_age_focal %>% filter(age_focal==30) %>% select(kin, total) -#' swe30_1950_nonstable$kin_by_age_focal %>% filter(age_focal==30) %>% select(kin, total) -#' } #' # get kin ---------------------------------------------------------------- kin <- function(U = NULL, f = NULL, diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R index ca1c305..18ed634 100644 --- a/R/kin_time_invariant.R +++ b/R/kin_time_invariant.R @@ -7,7 +7,7 @@ #' @param birth_female numeric. Female portion at birth. #' @param pi numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default `NULL`. #' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. -#' @param pi_stable logical. Want mean age at childbearing as a result too. Default `FALSE` +#' @param living logical. Only living kin counts `TRUE`, or including death kin `FALSE`. #' @param list_output logical. Results as a list. Default `FALSE` #' #' @return A data frame with focal´s age, related ages and type of kin @@ -18,7 +18,7 @@ kin_time_invariant <- function(U = NULL, f = NULL, birth_female = 1/2.04, pi = NULL, output_kin = NULL, - pi_stable = FALSE, + living = TRUE, list_output = FALSE){ # make matrix transition from vectors @@ -112,15 +112,18 @@ kin_time_invariant <- function(U = NULL, f = NULL, ) %>% reduce(rbind) - # add stable results? - if(pi_stable){ - out <- list(kin=kin, pi_stable=pi) - }else{ - out <- kin - } + # only living kin? + if(living) { + kin <- kin %>% filter(alive == "yes") + kin_list <- map(kin_list, function(x) {x[1:ages,]}) + } # results as list? - if(list_output) out <- kin_list + if(list_output) { + out <- kin_list + }else{ + out <- kin + } return(out) } diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index e92e7b2..2075878 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -36,6 +36,17 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, om <- max(age) zeros <- matrix(0, nrow=ages, ncol=ages) + # age distribution at childborn + if(is.null(pi)){ + if(is.null(N)){ + # create pi and fill it during the loop + message("Stable assumption was made for calculating pi on each year because no input data.") + pi <- matrix(0, nrow=ages, ncol=n_years_data) + }else{ + pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data))) + } + } + # get lists of matrix Ul = fl = list() for(t in 1:n_years_data){ @@ -51,22 +62,11 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, U <- Ul f <- fl - # age distribution at childborn - if(is.null(pi)){ - if(is.null(N)){ - # create pi and fill it during the loop - message("Stable assumption was made for calculating pi on each year because no input data.") - pi <- matrix(0, nrow=ages, ncol=n_years_data) - }else{ - pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data))) - } - } - # loop over years (more performance here) kin_all <- list() pb <- progress_bar$new( format = "Running over input years [:bar] :percent", - total = n_years_data, clear = FALSE, width = 60) + total = n_years_data, clear = FALSE, width = 10) for (iyear in 1:n_years_data){ # print(iyear) Ut <- as.matrix(U[[iyear]]) @@ -83,7 +83,8 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, U1 <- c(diag(Ut[-1,])[1:om],Ut[om,om]) f1 <- ft[1,][1:ages] pi1 <- pit[1:ages] - kin_all[[1]] <- kin_time_invariant(U = U1, f = f1, pi = pi1, birth_female = birth_female, list_output = TRUE) + kin_all[[1]] <- kin_time_invariant(U = U1, f = f1, pi = pi1, birth_female = birth_female, + list_output = TRUE, living = FALSE) } kin_all[[iyear+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[iyear]]) pb$tick() diff --git a/tests/repl_caswell.R b/tests/repl_caswell.R index 9fa372c..3b53057 100644 --- a/tests/repl_caswell.R +++ b/tests/repl_caswell.R @@ -4,46 +4,329 @@ library(devtools) load_all() library(DemoKin) library(tidyverse) +library(progress) +library(R.matlab) +load("tests/test.RData") -#time invariant +# basic +swe_kin_period_pack <- kin(U = swe_surv, + f = swe_asfr, + N = swe_pop, + time_invariant = F, + birth_female = 1, + output_period = c(1900, 1950, 2010), + output_kin = c("d","gd","m","gm","oa", "os")) -# Caswell's assumption on time invariant !!!!!!!!!!!!!!!!!!!!!! -ft[1,1:ages] = f * U * birth_female +swe_kin_period_pack$kin_full %>% + filter(alive == "yes") %>% + group_by(age_focal, kin, year) %>% + summarise(count = sum(count, na.rm=T)) %>% + ggplot(aes(age_focal, count, color=factor(year))) + + geom_line() + + facet_wrap(~kin, scales="free_y") -swe_surv_2010 <- swe_surv %>% pull(`2011`) -swe_asfr_2010 <- swe_asfr %>% pull(`2011`) -debugonce(kin) -swe50_2015_stable <- kin(U = swe_surv_2010, f = swe_asfr_2010, output_cohort = c(1911,1930), - output_kin = c("d","m")) +# time variant ------------------------------------------------------------ -swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, time_invariant = F, - birth_female = 1, - output_cohort = c(1911), - output_kin = c("d")) +# inputs +input_time_variant <- readMat("tests/SWEhist_matrices.mat") +input_time_variant_proj <- readMat("tests/SWEproj_matrices.mat") +# class(input_time_variant) +# names(input_time_variant) +# length(input_time_variant[["matrices"]]) # number of years +# input_time_variant[["matrices"]][[128]][[1]][[1]] # U +# input_time_variant[["matrices"]][[1]][[1]][[2]] # F +# input_time_variant[["matrices"]][[1]][[1]][[3]] # popsize +# input_time_variant[["matrices"]][[1]][[1]][[4]] # pi +# length(input_time_variant_proj[["matrices"]]) # number of years -U = U_caswell_2021; f = f_caswell_2021; pi = pi_caswell_2021; birth_female = 1; -output_cohort = c(1911);output_period = NULL; output_kin = c("d") +U_hal <- f_hal <-N_hal <- pi_hal <-matrix(rep(0,111)) +for(y in 1:128){ + # y = 1 + U <- input_time_variant[["matrices"]][[y]][[1]][[1]] %>% as.matrix() + f <- input_time_variant[["matrices"]][[y]][[1]][[2]] %>% as.matrix() + N <- input_time_variant[["matrices"]][[y]][[1]][[3]] %>% as.matrix() + pi <- input_time_variant[["matrices"]][[y]][[1]][[4]] %>% as.matrix() + U_hal <- cbind(U_hal, c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)])) + f_hal <- cbind(f_hal ,f[1,]) + N_hal <- cbind(N_hal ,N) + pi_hal <-cbind(pi_hal, pi) +} +U_hal_end <- U_hal[,-1] +f_hal_end <- f_hal[,-1] +N_hal_end <- N_hal[,-1] +pi_hal_end <-pi_hal[,-1] +colnames(U_hal_end) <- colnames(f_hal_end) <- colnames(N_hal_end) <- colnames(pi_hal_end) <-1891:2018 +dim(U_hal_end);class(U_hal_end %>% as.matrix) -# FIGURE 5 +# period +swe_kin_period <- kin(U = U_hal_end %>% as.matrix(), + f = f_hal_end %>% as.matrix(), + pi = pi_hal_end %>% as.matrix(), + time_invariant = F, + birth_female = 1, + output_period = c(1891,1921,1951,2010), + output_kin = c("d","gd","m","gm","oa", "os")) -# cohort -swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, pi = pi_caswell_2021, time_invariant = F, - birth_female = 1, - output_cohort = c(1891,1911,1931,1951,1971,2011,2041), - output_kin = c("d","gd","ggd","m","gm","ggm")) +# check first-row plots from figures 5-A and 5-B from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf +swe_kin_period$kin_full %>% + filter(alive == "yes") %>% + group_by(age_focal, kin, year) %>% + summarise(count = sum(count, na.rm=T)) %>% + ggplot(aes(age_focal, count, color=factor(year))) + + geom_line() + + facet_wrap(~kin, scales="free_y") -swe_kin_cohorts$kin_summary %>% - ggplot(aes(year,count,color=factor(cohort))) + - scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ - geom_line(size=1)+ - geom_vline(xintercept = 2019, linetype=2)+ - facet_wrap(~kin,scales = "free")+ +# read from https://www.dropbox.com/t/3YiILmn7SpczN3oM +output_time_variant <- readMat("tests/time-varying_sweden.mat") + +# inspect the way the package reads +# class(output_time_variant) +# names(output_time_variant) +# length(output_time_variant[["allkin"]]) # number of years +# length(output_time_variant[["allkin"]][[1]]) +# length(output_time_variant[["allkin"]][[1]]) +# class(output_time_variant[["allkin"]][[1]][[1]]) # 1 array with kin matrix +# dim(output_time_variant[["allkin"]][[1]][[1]][,,14]) # the matrix of the nth kin, 111 ages + +# use own codes to interpret +codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") +caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") + +# re arrange all data to a dataframe +output_time_variant_df <- map_df(1:128, function(i){ + array_branch(output_time_variant[["allkin"]][[i]][[1]], margin = 3) %>% + map_df(., as.data.frame)}) %>% + setNames(as.character(0:110)) %>% + bind_cols(crossing(year = 1891+(0:127), + kin_index = 1:14, + age_kin = 0:110)) %>% + inner_join(tibble(kin = codes, caswell_codes) %>% + arrange(caswell_codes) %>% mutate(kin_index = 1:14)) + +# check dimension: 128 years, 14 types of kin, 111 ages +nrow(output_time_variant_df); 128*14*111 + +# check first-row plots from figures 5-A and 5-B from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf +output_time_variant_df %>% + filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd", "m", "gm", "oa", "os")) %>% + pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>% + mutate(age = as.integer(age)) %>% + group_by(age, kin, year) %>% + summarise(count = sum(count)) %>% + ggplot(aes(age, count, color=factor(year))) + + geom_line() + + facet_wrap(~kin, scales="free_y") + +# differences - look d, gd, in 1891 and 1951 +swe_period_together <- swe_kin_period$kin_full %>% + filter(alive == "yes") %>% + filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd","m","gm","oa", "os")) %>% + group_by(age_focal, kin, year) %>% summarise(count_demokin = sum(count, na.rm=T)) %>% + inner_join( + output_time_variant_df %>% + filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd", "m", "gm","oa", "os")) %>% + pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>% + mutate(age = as.integer(age)) %>% + group_by(age_focal=age, kin, year) %>% + summarise(count_paper = sum(count))) + +swe_period_together %>% + filter(year == 1891) %>% + ggplot() + + geom_line(aes(age_focal, count_demokin, color=factor(year)), linetype=1) + + geom_line(aes(age_focal, count_paper, color=factor(year)), linetype=2) + + facet_wrap(~kin, scales="free_y") + +swe_period_rel_dif <- swe_period_together %>% + mutate(rel_dif = round(100*(count_paper/count_demokin-1),3)) %>% + arrange(year, kin) %>% + as.data.frame() %>% + group_by(year, kin) %>% summarise(sum(rel_dif, na.rm=T)) + + + + + + + + + + + + + + +# to bind projected +# U_hal <- U_hal[1:106,] +# f_hal <- f_hal[1:106,] +# N_hal <- N_hal[1:106,] +# pi_hal <-pi_hal[1:106,] +# for(y in 1:102){ +# # y = 1 +# U <- input_time_variant_proj[["matrices"]][[y]][[1]][[1]] +# f <- input_time_variant_proj[["matrices"]][[y]][[1]][[2]] +# N <- input_time_variant_proj[["matrices"]][[y]][[1]][[3]] +# pi <- input_time_variant_proj[["matrices"]][[y]][[1]][[4]] +# U_hal <- U_hal %>% bind_cols(c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)])) +# f_hal <- f_hal %>% bind_cols(f[1,]) +# N_hal <- N_hal %>% bind_cols(N) +# pi_hal <-pi_hal%>% bind_cols(as.numeric(pi)) +# } +# dim(U_hal[,-1]) +# U_hal_end <- U_hal[,-1] %>% setNames(as.character(1891:2120)) +# f_hal_end <- f_hal[,-1] %>% setNames(as.character(1891:2120)) +# N_hal_end <- N_hal[,-1] %>% setNames(as.character(1891:2120)) +# pi_hal_end <-pi_hal[,-1] %>% setNames(as.character(1891:2120)) +# dim(U_hal_end);names(U_hal_end) + +# time invariant ---------------------------------------------------------- + +### data: survival probability and fertility by age for Japan +# available at https://www.demographic-research.org/volumes/vol41/24/default.htm + +p_1947 <- 1 - read.csv("tests/qx_years.csv", header = F, sep = " ")[[4]] +f_1947 <- read.csv("tests/fx_years.csv", header = F, sep = " ")[[4]] +p_2014 <- 1 - read.csv("tests/qx_years.csv", header = F, sep = " ")[[205]] +f_2014 <- read.csv("tests/fx_years.csv", header = F, sep = " ")[[205]] + +# Caswell assumption on first age +f_1947 <- f_1947 * p_1947 +f_2014 <- f_2014 * p_2014 + +kins_japan_1947 <- kin(p_1947, f_1947, living = F)$kin_full +kins_japan_1947 %>% + filter(alive=="yes", kin=="ggm") %>% + group_by(age_focal) %>% summarise(sum(count)) + + +### results +kins_japan <- rbind(tibble(Year = 1947, kin(p_1947, f_1947, living = F)$kin_full), + tibble(Year = 2014, kin(p_2014, f_2014, living = F)$kin_full)) + +# kins alive by age when ego is aged 30 or 70 +kins_japan %>% + filter(age_focal %in% c(30,70), alive=="yes") %>% + ggplot() + + geom_line(aes(x=age_kin, y=count, + color=factor(age_focal), linetype=factor(Year))) + + facet_wrap(~kin,scales = "free_y") + + theme_classic() + + facet_wrap(~kin,scales = "free_y") + +kins_japan %>% + filter(age_focal %in% 30, alive=="yes", kin == "m", Year==2014) + +### get paper results: done with https://plotdigitizer.com/app + +m_30_2014 <- c(48.124993716677295, 0.0068724848600042006, + 52.13541022398433, 0.022765097394085585, + 56.14582673129136, 0.056697985757917374, + 60.04166165822103, 0.07398657677157613, + 64.16665974590543, 0.054765100671140945, + 68.17707625321246, 0.02330201014576342, + 71.95832959976478, 0.0035436192454907658) %>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "m", year = 2014, age = 30) +m_30_1947 <- c(47.89583055592257, 0.010630874121749168, + 51.791665482852245, 0.045100671140939595, + 56.37499863406029, 0.05111409232120386, + 59.92708007784368, 0.03908724586435613, + 63.82291500477335, 0.02577181208053692, + 68.17707625321246, 0.012671136024014266, + 71.84374801938742, 0.0025771828465813683) %>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "m", year = 1947, age = 30) +s_30_1947 <- c(117.30467373388943, 0.028030307190039253, + 3.8671896129379024, 0.0011363654972982584, + 8.05663990468026, 0.005681817853417949, + 12.031249743886312, 0.01792929767285178, + 16.220700035628674, 0.036111111913867226, + 19.873045098211307, 0.05782828333912762, + 23.84765903523633, 0.07626262618964844, + 28.037105229159724, 0.08244949644554042, + 32.119139392452894, 0.06717171906914071, + 36.09374513383999, 0.044696973856705915, + 39.853510422690775, 0.024621210698144477, + 43.93554458598395, 0.010858592937435215, + 47.80273010110288, 0.00303030478177092) %>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "s", year = 1947, age = 30) +s_70_1947 <- c(43.93554458598395, 0.0011363654972982584, + 47.80273010110288, 0.00441919166376952, + 52.20702494320051, 0.013383845316732092, + 56.074218653957374, 0.026388889290266948, + 59.94140416907631, 0.03952020358922534, + 64.02343013673156, 0.048358586916764375, + 68.10546430002474, 0.045959600046354354, + 71.97264981514367, 0.03143939886539736, + 76.05468397843684, 0.015404045293554923, + 80.02928971982392, 0.005429296468717607, + 84.00390365684893, 0.0011363654972982584) %>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "s", year = 1947, age = 70) +s_30_2014 <- c(7.949219678412107, 0.0005050524024740438, + 12.031249743886312, 0.0032828357995446046, + 16.005859583092366, 0.009217175037662914, + 19.873045098211307, 0.020328289359798468, + 23.6328103870621, 0.03194444645133473, + 27.822264776623417, 0.03888889049440111, + 32.01171916618474, 0.0337121250434572, + 35.77148445503553, 0.0215909155494469, + 39.96093884459685, 0.010732322612011683, + 44.04296481225208, 0.0032828357995446046)%>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "s", year = 2014, age = 30) +s_70_2014 <- c(51.88476426439605, 0.002904044089420749, + 56.18163888022552, 0.009343435730013084, + 59.83398394280815, 0.01944444524720057, + 64.13085855863763, 0.03068182026168629, + 68.21288452629288, 0.035479798819043, + 71.75780936260736, 0.03068182026168629, + 76.16210420470499, 0.019318184554850383, + 79.92186949355577, 0.008459601250488509, + 84.00390365684893, 0.00265152270472039) %>% matrix(ncol=2, byrow = T) %>% + as.data.frame() %>% setNames(c("age_kin","count")) %>% + mutate(kin = "s", year = 2014, age = 70) + +output_time_invariant <- m_30_2014 %>% + bind_rows(m_30_1947, s_30_1947, s_70_1947, s_30_2014, s_70_2014) %>% + mutate(age_kin = trunc(age_kin), count=round(count,7)) + +compare_time_invariant <- kins_japan %>% + filter(kin %in% c("os", "ys"), alive == "yes") %>% + group_by(Year, age_focal, age_kin, alive) %>% + summarise(count = sum(count)) %>% + mutate(kin = "s") %>% + bind_rows(kins_japan %>% filter(alive == "yes")) %>% + select(-year, -cohort, -alive) %>% + rename(count_demokin = count, year = Year) %>% + mutate(count_demokin = round(count_demokin,7)) %>% + right_join(output_time_invariant %>% + rename(age_focal=age, count_paper = count)) + +compare_time_invariant %>% + ggplot() + + geom_line(aes(age_kin, count_demokin, linetype=factor(year)), col=1)+ + geom_line(aes(age_kin, count_paper, linetype=factor(year)), col=2) + + facet_grid(~kin+age_focal)+ theme_bw() + +### compare values + + + + + + + + + + # period swe_kin_period <- kin(U = U_caswell_2021, f = f_caswell_2021, pi = pi_caswell_2021, stable = F, birth_female = 1, - focal_year = c(1891,1921,1951,2010,2050,2080,2120), - selected_kin = c("d","gd","ggd","m","gm","ggm","os","ys","oa","ya")) + focal_year = c(1891,1921,1951,2010,2050,2080,2120), + selected_kin = c("d","gd","ggd","m","gm","ggm","os","ys","oa","ya")) swe_kin_period$kin_summary %>% ggplot(aes(age_focal,count,color=factor(year))) + @@ -73,3 +356,87 @@ swe_kin_period$kin_summary %>% coord_fixed()+ labs(title = "Kin period") +### plots +# kins alive by age when ego is aged 30 or 70 +kins_japan %>% + filter(age_focal %in% c(30,70), alive=="yes") %>% + ggplot() + + geom_line(aes(x=age_kin, y=count, + color=factor(age_focal), linetype=factor(Year))) + + facet_wrap(~kin,scales = "free_y") + + theme_classic() + + facet_wrap(~kin,scales = "free_y") +# kins alive during ego´s life +kins_japan %>% + filter(alive=="yes") %>% + group_by(Year, kin, age_focal) %>% summarise(count = sum(count)) %>% + ggplot() + + geom_line(aes(age_focal, count, linetype=factor(Year))) + + theme_classic() + + facet_wrap(~kin, scales = "free_y") +# experienced deaths +kins_japan %>% + filter(alive=="no") %>% + group_by(Year, kin, age_focal) %>% summarise(count = sum(count)) %>% + ggplot() + + geom_line(aes(age_focal, count, linetype=factor(Year))) + + theme_classic() + + facet_wrap(~kin, scales = "free_y") +# variation coefficient of age by kin +kins_japan %>% + filter(alive=="yes") %>% + group_by(Year, kin, age_focal) %>% + summarise(mean_age = sum(count*age_kin)/sum(count), + var_age = sum(count*age_kin^2)/sum(count) - mean_age^2, + cv_age = round(sqrt(var_age)/mean_age*100,1)) %>% + ggplot() + + geom_line(aes(age_focal, cv_age, linetype=factor(Year))) + + theme_classic() + + facet_wrap(~kin, scales = "free_y") +# dependency ages +kins_japan %>% + filter(alive=="yes") %>% + mutate(age_kin_dep = ifelse(age_kin<15,"0-14", + ifelse(age_kin<65,"15-64","65+"))) %>% + group_by(Year, kin, age_focal, age_kin_dep) %>% + summarise(count = sum(count)) %>% + ggplot() + + geom_line(aes(age_focal, count, + color = age_kin_dep, linetype=factor(Year))) + + theme_classic() + + facet_wrap(~kin, scales = "free_y") + + + + + + + + + + + + + + + + + +swe_surv_2010 <- swe_surv %>% pull(`2011`) +swe_asfr_2010 <- swe_asfr %>% pull(`2011`) +debugonce(kin) +swe50_2015_stable <- kin(U = swe_surv_2010, f = swe_asfr_2010, output_cohort = c(1911,1930), + output_kin = c("d","m")) + +swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, time_invariant = F, + birth_female = 1, + output_cohort = c(1911), + output_kin = c("d")) + +U = U_caswell_2021; f = f_caswell_2021; pi = pi_caswell_2021; birth_female = 1; +output_cohort = c(1911);output_period = NULL; output_kin = c("d") + +# FIGURE 5 + + + diff --git a/tests/repl_caswell_first_year.R b/tests/repl_caswell_first_year.R new file mode 100644 index 0000000..c88c121 --- /dev/null +++ b/tests/repl_caswell_first_year.R @@ -0,0 +1,106 @@ +# replicating Caswell´s figures: choose some kin +library(DemoKin) +library(tidyverse) +library(R.matlab) +source("R/kin_time_invariant.R") + +# paper input from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf +input_time_variant <- readMat("tests/SWEhist_matrices.mat") + +# check structure from reading mat +class(input_time_variant) +names(input_time_variant) +length(input_time_variant[["matrices"]]) # number of years +input_time_variant[["matrices"]][[128]][[1]][[1]] # U +input_time_variant[["matrices"]][[1]][[1]][[2]] # F +input_time_variant[["matrices"]][[1]][[1]][[3]] # popsize +input_time_variant[["matrices"]][[1]][[1]][[4]] # pi +length(input_time_variant_proj[["matrices"]]) # number of years + +# reshape +U_hal <- f_hal <-N_hal <- pi_hal <-matrix(rep(0,111)) +for(y in 1:128){ + U <- input_time_variant[["matrices"]][[y]][[1]][[1]] %>% as.matrix() + f <- input_time_variant[["matrices"]][[y]][[1]][[2]] %>% as.matrix() + N <- input_time_variant[["matrices"]][[y]][[1]][[3]] %>% as.matrix() + pi <- input_time_variant[["matrices"]][[y]][[1]][[4]] %>% as.matrix() + U_hal <- cbind(U_hal, c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)])) + f_hal <- cbind(f_hal ,f[1,]) + N_hal <- cbind(N_hal ,N) + pi_hal <-cbind(pi_hal, pi) +} +U_hal <- U_hal[,-1] +f_hal <- f_hal[,-1] +N_hal <- N_hal[,-1] +pi_hal <-pi_hal[,-1] +colnames(U_hal) <- colnames(f_hal) <- colnames(N_hal) <- colnames(pi_hal) <-1891:2018 +dim(U_hal);class(U_hal %>% as.matrix) + +# output from Hal (dropbox link https://www.dropbox.com/t/3YiILmn7SpczN3oM) +output_time_variant <- readMat("tests/time-varying_sweden.mat") + +# inspect the way the package reads mat +class(output_time_variant) +names(output_time_variant) +length(output_time_variant[["allkin"]]) # number of years +length(output_time_variant[["allkin"]][[1]]) +length(output_time_variant[["allkin"]][[1]]) +class(output_time_variant[["allkin"]][[1]][[1]]) # 1 array with kin matrix +dim(output_time_variant[["allkin"]][[1]][[1]][,,14]) # the matrix of the nth kin, 111 ages + +# use own codes to interpret +codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") +caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") + +# re shape data to tidy +output_time_variant_df <- map_df(1:128, function(i){ + array_branch(output_time_variant[["allkin"]][[i]][[1]], margin = 3) %>% + map_df(., as.data.frame)}) %>% + setNames(as.character(0:110)) %>% + bind_cols(crossing(year = 1891+(0:127), # years + kin_index = 1:14, # number of possible kin + age_kin = 0:110) # ages + ) %>% + inner_join(tibble(kin = codes, caswell_codes) %>% + arrange(caswell_codes) %>% mutate(kin_index = 1:14)) + +# check dimension: 128 years, 14 types of kin, 111 ages +nrow(output_time_variant_df); 128*14*111 + +# own calculation for first year +out_first_year <- kin_time_invariant( + U = U_hal[,"1891"], + f = f_hal[,"1891"], + pi = pi_hal[,"1891"], + birth_female = 1) + +# check first visually demokin +out_first_year %>% + filter(alive == "yes") %>% + group_by(age_focal, kin) %>% + summarise(count = sum(count, na.rm=T)) %>% + ggplot(aes(age_focal, count)) + + geom_line() + + facet_wrap(~kin, scales="free_y") + +# compare with paper results +comparison <- out_first_year %>% + filter(alive == "yes") %>% + group_by(age_focal, kin) %>% + summarise(count = sum(count, na.rm=T)) %>% + mutate(source = "demokin") %>% + bind_rows( + output_time_variant_df %>% + filter(year %in% 1891) %>% + pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>% + mutate(age = as.integer(age)) %>% + group_by(age_focal=age, kin) %>% + summarise(count = sum(count)) %>% + mutate(source = "paper")) + +# comparison visually +comparison %>% + ggplot() + + geom_line(aes(age_focal, count, color=source, linetype=source)) + + facet_wrap(~kin, scales="free_y") + + theme_bw() diff --git a/vignettes/Use.Rmd b/vignettes/Use.Rmd index 07f1158..2d50bc4 100644 --- a/vignettes/Use.Rmd +++ b/vignettes/Use.Rmd @@ -14,27 +14,29 @@ devtools::load_all() library(tidyverse) library(DiagrammeR) library(knitr) +library(progress) library(igraph) ``` -The year is 1995 and focal is 35 years old. Focal and her relatives have all lived in Sweden. -We would like to know how many female relatives focal has in 1995, their mean age and the number of living kin at each age of focal. Finally, we would like to know at which age she had to suffer a death in average. +The year is 2015 and focal is 35 years old. Focal and her relatives have all lived in Sweden. +We would like to know how many female relatives focal has in 2015, their mean age and the number of living kin at each age of focal. Finally, we would like to know at which age she had to suffer a death in average. -## Kin counts in stable populations +## Kin counts with time invariant rates -We can estimate this in a **stable** framework using data from 1995, assuming that all of focal's relatives experience mortality and fertility from that calendar year (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example (*swe_surv*, *swe_asfr* and *swe_pop*, for survival, fertility and population; type `data(package="DemoKin")`). This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/). These datasets were loaded using the`get_HMDHFD` function. +We can estimate this in a **stable** framework using data from 2015, assuming that all of focal's relatives experience mortality and fertility from that calendar year (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example (*swe_surv*, *swe_asfr* and *swe_pop*, matrix of age by year, with survival, fertility and population; type `data(package="DemoKin")`). This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/). These datasets were loaded using the`get_HMDHFD` function. ```{r} -swe35_2015_stable <- kin(U = swe_surv, f = swe_asfr, focal_year = 1995, stable = TRUE) +swe_surv_2015 <- swe_surv[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] +swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` Let's visualize the distribution of relatives over focal's lifecourse: ```{r, fig.height=6, fig.width=8} -swe35_2015_stable[["kin_summary"]] %>% +swe_2015[["kin_summary"]] %>% ggplot() + geom_line(aes(age_focal, count)) + - geom_vline(xintercept = 35, color=2)+ theme_bw() + facet_wrap(~kin) ``` @@ -63,10 +65,10 @@ data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) %>% ``` -We can also visualize the age distribution of relatives when focal is 35: +We can also visualize the age distribution of relatives when focal is 35 for example: ```{r, fig.height=6, fig.width=8} -swe35_2015_stable[["kin_full"]] %>% +swe_2015[["kin_full"]] %>% filter(age_focal == 35) %>% ggplot() + geom_line(aes(age_kin, count)) + @@ -74,36 +76,42 @@ swe35_2015_stable[["kin_full"]] %>% facet_wrap(~kin) ``` -The function also includes data on the mean age of focal's relatives at her actual age: +The function assumes that fertility input: +- is for both sex. In case is only for one you need to set `birth_female = 1`. +- considers *effective* births. This is important in the case you are using survival rates ($S_x$) instead of probabilities ($p_x$). In the case your are using survival and you do not want all new born survives to truncated age 0 (it means $P_b=1$ in life table notation), fertility rates must consider this risk already. This should be done for example in the previous case, using as input `swe_asfr * $P_b$` for fertility. In the case of probabilities newborn would have 0 exact age, and no additional risk must be considered, + +The function also includes a summary output with the count of living kin, mean and standard devaition of kin age, by type of kin, for each focal's age: ```{r, fig.height=6, fig.width=8} -swe35_2015_stable[["kin_summary"]] %>% +swe_2015[["kin_summary"]] %>% filter(age_focal == 35) %>% select(kin, count, mean_age, sd_age) %>% + mutate_if(is.numeric, round, 2) %>% kable() ``` -We can visualize the estimated kin counts for focal at age 35 in a stable population using a network diagram: +We can visualize the estimated kin counts by kin using a network diagram. Folloing with the age 35: ```{r} -kin_total <- swe35_2015_stable[["kin_summary"]] %>% +swe_2015_age35 <- swe_2015[["kin_summary"]] %>% filter(age_focal == 35) %>% select(kin, count) -plot_diagram(kin_total) +plot_diagram(swe_2015_age35) ``` -## Kin counts in non-stable populations -The population of Sweden is, in reality, **non-stable**, and focal' relatives have been expossed to changing mortality and fertility rates. -To account for this, is needed estimattions of kin counts in population with time-variant demographic rates. For this we adapted Matlab´s code from Caswell (2021). Let's take a look at the resulting kin counts at focal's age 35 using empirical single-year mortality and fertility rates as input: +## Kin counts with time varying rates + +The demographic risks of population of Sweden is, in reality, changin every year, and focal relatives have been expossed to changing mortality and fertility rates. +To account for this, is needed estimations of kin counts in population with time-variant demographic rates. Let's take a look at the resulting kin counts for 1960 cohort: ```{r, fig.height=6, fig.width=8} -swe35_2015_nonstable <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, stable=FALSE, - focal_cohort = 1960, - selected_kin = c("d","gd","ggd","m","gm","ggm")) +swe_2015_time_varying <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, time_invariant =FALSE, + output_cohort = 1960, + output_kin = c("d","gd","ggd","m","gm","ggm")) -swe35_2015_nonstable$kin_full %>% +swe_2015_time_varying$kin_full %>% group_by(kin, cohort, age_focal) %>% summarise(count = sum(count)) %>% ggplot(aes(age_focal,count,color=factor(cohort))) + scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ @@ -114,30 +122,33 @@ swe35_2015_nonstable$kin_full %>% ``` +As you see the estimates stop at age 55, because no data is after 2015. + ## Death Experience -Kin loss has studied consequences related to mental health, care support and household incomes over life course. Setting the parameter `alive="no"` we can see how many relatives focal loss during her life (output `kin_death_by_age_focal`), in a stable framework: +Kin loss has studied consequences related to mental health, care support and household incomes over life course. Setting the parameter `alive="no"` we can see for a focal from 1900 cohort how many relatives loss during her life (output `kin_death_by_age_focal`): ```{r, fig.height=6, fig.width=8} -swe35_2015_nonstable <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, stable=FALSE, - focal_cohort = 1900, - selected_kin = c("d","gd","ggd","m","gm","ggm"), +swe_1900_time_varying <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, time_invariant = FALSE, + output_cohort = 1900, + output_kin = c("d","gd","ggd","m","gm","ggm"), living = FALSE) -swe35_2015_nonstable$kin_summary %>% +swe_1900_time_varying$kin_summary %>% filter(alive =="no") %>% ggplot() + - geom_line(aes(age_focal, count_cum, color=cohort)) + + geom_line(aes(age_focal, count_cum)) + theme_bw() + facet_wrap(~kin,scales="free") ``` -Given some portion of each realtive was dead (remember we are talking about population measures), we can ask what was the mean age at lost for each kin: +Given some portion of each relative was dead (remember we are talking about population measures), we can ask what was the mean age at lost for each kin, when focal was 50: ```{r} -swe35_2015_nonstable$kin_summary %>% +swe_1900_time_varying$kin_summary %>% filter(alive=="no", age_focal == 50) %>% select(kin,count_cum,mean_age_lost) %>% + mutate_if(is.numeric, round, 2) %>% kable() ``` From fc3fe12a099e5c1912cac60635ff0516a4197433 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Fri, 19 Aug 2022 13:23:59 -0300 Subject: [PATCH 04/21] px and Sx now, add Xi ctb --- DESCRIPTION | 9 +- NAMESPACE | 4 +- R/data.R | 11 +- R/get_HMDHFD.R | 110 ++++++--- R/kin.R | 8 +- R/kin_time_variant.R | 8 +- README.Rmd | 25 +- README.md | 34 +-- data/swe_Sx.rda | Bin 0 -> 85651 bytes data/swe_asfr.rda | Bin 18231 -> 23276 bytes data/swe_pop.rda | Bin 28925 -> 38380 bytes data/swe_px.rda | Bin 0 -> 41614 bytes dev/kin_multi_stage.R | 223 ++++++++++++++++++ dev/multi_stage_tests.R | 115 --------- {tests => dev/tests}/repl_caswell.R | 1 + .../tests}/repl_caswell_first_year.R | 0 dev/tests/timevarying_kin.m | 180 ++++++++++++++ man/get_HMDHFD.Rd | 21 +- man/kin.Rd | 44 +--- man/kin_non_stable.Rd | 44 ---- man/kin_stable.Rd | 38 --- man/kin_time_invariant.Rd | 38 +++ man/kin_time_variant.Rd | 41 ++++ man/{swe_surv.Rd => swe_Sx.Rd} | 6 +- man/swe_px.Rd | 19 ++ man/timevarying_kin.Rd | 2 +- vignettes/Use.Rmd | 52 +++- 27 files changed, 689 insertions(+), 344 deletions(-) create mode 100644 data/swe_Sx.rda create mode 100644 data/swe_px.rda create mode 100644 dev/kin_multi_stage.R delete mode 100644 dev/multi_stage_tests.R rename {tests => dev/tests}/repl_caswell.R (99%) rename {tests => dev/tests}/repl_caswell_first_year.R (100%) create mode 100644 dev/tests/timevarying_kin.m delete mode 100644 man/kin_non_stable.Rd delete mode 100644 man/kin_stable.Rd create mode 100644 man/kin_time_invariant.Rd create mode 100644 man/kin_time_variant.Rd rename man/{swe_surv.Rd => swe_Sx.Rd} (89%) create mode 100644 man/swe_px.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 26562b8..c1d035e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,20 +1,21 @@ Package: DemoKin Title: Demokin -Description: Estimate kin counts for stable and non stable populations +Description: Estimate population kin counts and its distribution by type and age Version: 0.0.0.9000 Authors@R: c( person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"), - person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut")) + person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"), + person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb")) License: MIT + file LICENSE Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.1.1 +RoxygenNote: 7.2.1 Suggests: knitr, rmarkdown VignetteBuilder: knitr -Imports: tidyverse, DiagrammeR, HMDHFDplus, devtools +Imports: tidyverse, DiagrammeR, HMDHFDplus, devtools, progress BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index 8e89f40..d0806d3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,7 +2,7 @@ export(get_HMDHFD) export(kin) -export(kin_non_stable) -export(kin_stable) +export(kin_time_invariant) +export(kin_time_variant) export(plot_diagram) importFrom(DiagrammeR,mermaid) diff --git a/R/data.R b/R/data.R index 949e55b..2fd5f27 100644 --- a/R/data.R +++ b/R/data.R @@ -7,7 +7,16 @@ #' #' @source #' HMD/HFD -"swe_surv" +"swe_Sx" + +#' Female swedish survival probabilities from 1900 to 2015 +#' @docType data +#' @format +#' A matrix with years as cols and ages (0 to 100 as OAG) as rows. +#' +#' @source +#' HMD/HFD +"swe_px" #' Swedish age-specific fertility rates from 1900 to 2015 #' diff --git a/R/get_HMDHFD.R b/R/get_HMDHFD.R index ce8aa41..d518eec 100644 --- a/R/get_HMDHFD.R +++ b/R/get_HMDHFD.R @@ -1,89 +1,125 @@ #' Get time serie matrix data from HMD/HFD -#' @description Wrapper function to get data.frames of survival, fertlity and population +#' @description Wrapper function to get data.frames of female survival, fertlity and population #' of selected countriy on selected period. #' @param country numeric. Country code from rom HMD/HFD. #' @param max_year numeric. Latest year to get data. #' @param min_year integer. Older year to get data. #' @param user character. From HMD/HFD. -#' @param pass character. From HMD/HFD. -#' -#' @return A list wiith survival, fertility and poopulation age specific matrixes, with calendar year as colnames. +#' @param user_HMD character. From HMD/HFD. +#' @param user_HFD character. From HMD/HFD. +#' @param pass_HMD character. From HMD/HFD. +#' @param pass_HFD character. From HMD/HFD. +#' @return A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames. #' @export get_HMDHFD <- function(country = "SWE", min_year = 1900, max_year = 2018, - user = NULL, - pass = NULL){ + user_HMD = NULL, + pass_HMD = NULL, + user_HFD = NULL, + pass_HFD = NULL, + OAG = 100){ - # source HMD HFD - use SWE now----------------------------------------------------------------- - pop <- readHMDweb(CNTRY = country, "Population", user, pass, fixup = TRUE) %>% + if(any(c(is.null(user_HMD), is.null(user_HFD), is.null(pass_HMD), is.null(pass_HFD)))){ + stop("The function needs HMD and HMF access.") + } + + # source HMD HFD ----------------------------------------------------------------- + pop <- readHMDweb(CNTRY = country, "Population", user_HMD, pass_HMD, fixup = TRUE) %>% select(Year, Age, N = Female1)%>% filter(Year >= min_year, Year <= max_year) - lt <- readHMDweb(country, "fltper_1x1", user, pass, fixup = TRUE) %>% + lt <- readHMDweb(country, "fltper_1x1", user_HMD, pass_HMD, fixup = TRUE) %>% filter(Year >= min_year, Year <= max_year) - asfr <- readHFDweb(country, "asfrRR", user, "52962", fixup = TRUE)%>% + asfr <- readHFDweb(country, "asfrRR", user_HFD, pass_HFD, fixup = TRUE)%>% filter(Year >= min_year, Year <= max_year) # list of yearly Leslie matrix --------------------------------------------------- - age = 0:100 + age = 0:OAG ages = length(age) w = last(age) last_year = max(lt$Year) years = min_year:last_year - # survival probabilities - L <- lt %>% - filter(Age<=w) %>% - mutate(Lx = ifelse(Age==w, Tx, Lx)) %>% + # survival probability + px <- lt %>% + filter(Age<=OAG) %>% + mutate(px = 1 - qx, + px = ifelse(Age==OAG, 0, px)) %>% + select(Year, Age, px) %>% + pivot_wider(names_from = "Year", values_from = "px") %>% + select(-Age) %>% + as.matrix() + rownames(px) = 0:OAG + + # survival function + Lx <- lt %>% + filter(Age<=OAG) %>% + mutate(Lx = ifelse(Age==OAG, Tx, Lx)) %>% select(Year, Age, Lx) %>% - spread(Year, Lx) %>% - select(-Age) + pivot_wider(names_from = "Year", values_from = "Lx") %>% + select(-Age) %>% + as.matrix() - P <- rbind(L[c(-1,-101),]/L[-c(100:101),], - L[101,]/(L[100,]+L[101,]), - L[101,]/(L[100,]+L[101,])) - rownames(P) = 0:100 + Sx <- rbind(Lx[c(-1,-ages),]/Lx[-c(w:ages),], + Lx[ages,]/(Lx[w,]+Lx[ages,]), + Lx[ages,]/(Lx[w,]+Lx[ages,])) + rownames(Sx) = 0:w # fertility - asfr <- asfr %>% + fx <- asfr %>% filter(Year >= min_year) %>% select(-OpenInterval) %>% rbind( expand.grid(Year = years, - Age = c(0:11,56:100), + Age = c(0:(min(asfr$Age)-1),(max(asfr$Age)+1):OAG), ASFR = 0)) %>% arrange(Year, Age) %>% spread(Year, ASFR) %>% - select(-Age) - rownames(asfr) = 0:100 + select(-Age) %>% + as.matrix() + rownames(fx) = 0:OAG # population - N <- pop %>% - mutate(Age = ifelse(Age>100, 100, Age)) %>% + Nx <- pop %>% + mutate(Age = ifelse(Age>OAG, OAG, Age)) %>% group_by(Year, Age) %>% summarise(N = sum(N)) %>% - filter(Age<=100, Year >= min_year) %>% + filter(Age<=OAG, Year >= min_year) %>% arrange(Year, Age) %>% spread(Year, N) %>% - select(-Age) - rownames(N) = 0:100 + select(-Age) %>% + as.matrix() + rownames(Nx) = 0:OAG # only return data with values - if(any(is.na(colSums(P)))){ + if(any(is.na(colSums(Sx)))){ warning("Asked for data out of HMDHFD range") - P <- P[,!is.na(colSums(P))] + Sx <- Sx[,!is.na(colSums(Sx))] } - if(any(is.na(colSums(asfr)))){ + if(any(is.na(colSums(fx)))){ warning("Asked for data out of HMDHFD range") - asfr <- asfr[,!is.na(colSums(asfr))] + fx <- fx[,!is.na(colSums(fx))] } - if(any(is.na(colSums(N)))){ + if(any(is.na(colSums(Nx)))){ warning("Asked for data out of HMDHFD range") - N <- N[,!is.na(colSums(N))] + Nx <- Nx[,!is.na(colSums(Nx))] } - return(list(P=P, asfr=asfr, N=N)) + return(list(px=px, + Sx=Sx, + fx=fx, + Nx=Nx)) } + +# save data + # swe_px <- swe_data$px + # swe_Sx <- swe_data$Sx + # swe_asfr <-swe_data$fx + # swe_pop <- swe_data$Nx + # save(swe_px, file = "data/swe_px.rda") + # save(swe_Sx, file = "data/swe_Sx.rda") + # save(swe_asfr, file = "data/swe_asfr.rda") + # save(swe_pop, file = "data/swe_pop.rda") diff --git a/R/kin.R b/R/kin.R index 70e1497..a111d9d 100644 --- a/R/kin.R +++ b/R/kin.R @@ -4,7 +4,7 @@ #' @details See Caswell (2019) and Caswell (2021) for details on formulas. #' @param U numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). #' @param f numeric. Same as U but for fertility rates. -#' @param time_invariant logical. Constant assumption for a given `year` rates. +#' @param time_invariant logical. Constant assumption for a given `year` rates. Defautk `TRUE`. #' @param N numeric. Same as U but for population distribution (counts or `%`). Optional. #' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional. #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range. @@ -43,9 +43,9 @@ kin <- function(U = NULL, f = NULL, # if time dependent or not if(time_invariant){ if(!is.vector(U)) { - focal_year <- min(years_data) - U <- U[,as.character(focal_year)] - f <- f[,as.character(focal_year)] + output_period <- min(years_data) + U <- U[,as.character(output_period)] + f <- f[,as.character(output_period)] } kin_full <- kin_time_invariant(U = U, f = f, output_kin = output_kin, birth_female = birth_female) %>% diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index 2075878..09465cc 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -7,12 +7,12 @@ #' @param N numeric. A matrix of population with rows as ages and columns as years. Coincident with `U`. #' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with `U`. #' @param output_cohort integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range. -#' @param output_period integer. Year of focal to return as output. Could be a vector. Should be within input data years range. +#' @param output_period integer. Year for which to return kinship structure. Could be a vector. Should be within input data years range. #' @param output_kin character. kin to return as output: "m" for mother, "d" for daughter,... See `vignette` for exahustive kin. #' @param birth_female numeric. Female portion at birth. #' -#' @return A data frame with focal´s age, related ages and type of kin -#' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. +#' @return A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives +#' (for example `d` is daughter, `oa` is older aunts, etc.), and age of (living or deceased) relatives. #' @export kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, @@ -66,7 +66,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, kin_all <- list() pb <- progress_bar$new( format = "Running over input years [:bar] :percent", - total = n_years_data, clear = FALSE, width = 10) + total = n_years_data, clear = FALSE, width = 60) for (iyear in 1:n_years_data){ # print(iyear) Ut <- as.matrix(U[[iyear]]) diff --git a/README.Rmd b/README.Rmd index 9413c44..353c905 100644 --- a/README.Rmd +++ b/README.Rmd @@ -14,7 +14,7 @@ knitr::opts_chunk$set( library(tidyverse) library(DiagrammeR) library(knitr) -# library(igraph) +library(igraph) devtools::load_all() library(DemoKin) ``` @@ -34,13 +34,6 @@ devtools::install_github("IvanWilli/DemoKin") ## Example -``` {r,eval=F,include=F} -library(tidyverse) -library(DiagrammeR) -library(knitr) -``` - - Consider an average Swedish woman in year 2015 (i.e., 'Focal'). Relatives for the `selected_kin` argument are identified by a unique code. Note that the relationship codes used in `DemoKin` differ from those in Caswell (2019). @@ -66,20 +59,14 @@ For this exercise, we'll use the Swedish data pre-loaded with `DemoKin`. ```{r, fig.height=6, fig.width=8} library(DemoKin) -swe50_2015_stable <- kin(U = swe_surv, f = swe_asfr, focal_year = 2015, stable = TRUE) +swe_surv_2015 <- swe_px[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] +swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` -*U* is the survival ratio by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). - -We can visualize the implied kin counts for an Focal aged 35 yo in a stable population using a network or 'Keyfitz' kinship diagram using the `plot_diagram` function: - -```{r} -plot_diagram(swe50_2015_stable[["kin_summary"]] %>% - filter(age_focal == 35) %>% - select(kin, count)) -``` +*px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). -For more details, including an extension to non-stable populations and relative´s death distribution, see `vignette("Use")`. +For more details, including an extension to time varying populations rates and relative´s death distribution, see `vignette("Use")`. Note that if the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation diff --git a/README.md b/README.md index f67834a..4b60992 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ those in Caswell (2019). The equivalence between the two set of codes is given in the following table: | DemoKin | Caswell | Label | -| :------ | :------ | :------------------------- | +|:--------|:--------|:---------------------------| | coa | t | Cousins from older aunt | | cya | v | Cousins from younger aunt | | d | a | Daughter | @@ -56,28 +56,18 @@ For this exercise, we’ll use the Swedish data pre-loaded with `DemoKin`. ``` r library(DemoKin) -swe50_2015_stable <- kin(U = swe_surv, f = swe_asfr, focal_year = 2015, stable = TRUE) +swe_surv_2015 <- swe_px[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] +swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` -*U* is the survival ratio by age from a life table and *f* are the age -specific fertility ratios by age (see `?kin` for details). +*px* is the survival probability by age from a life table and *f* are +the age specific fertility ratios by age (see `?kin` for details). -We can visualize the implied kin counts for an Focal aged 35 yo in a -stable population using a network or ‘Keyfitz’ kinship diagram using the -`plot_diagram` function: - -``` r -plot_diagram(swe50_2015_stable[["kin_summary"]] %>% - filter(age_focal == 35) %>% - select(kin, count)) -``` - - - -For more details, including an extension to non-stable populations and -relative´s death distribution, see `vignette("Use")`. Note that if the -vignette does not load, you may need to install the package as -`devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. +For more details, including an extension to time varying populations +rates and relative´s death distribution, see `vignette("Use")`. Note +that if the vignette does not load, you may need to install the package +as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation @@ -106,8 +96,8 @@ Population Biology 5(1):1–27. . 3. Improve kinship diagram visualization 4. Improve documentation and vignette of package -## Get involved\! +## Get involved! `DemoKin` is giving its first steps. If you’re interested in contributing, please get in touch, create an issue, or submit a pull -request. We look forward to hearing from you\! +request. 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zQ9%VCBBU!iaPmU?ZWUOy(g#2VT`gRfo{AVub#3TH5UM~|toB_w@pkz|gWu{*Evft8%uTCM)6PR=Y>(%M0*{oG0&0lBAQNcE{6eP0ABpo0)giaRS==m<_PS*l;%W{# zgC=_vr8pNEN05`kS=l+gTo>IGT3AnOJZi;3iE-_zixq*j6 zO0A&lGrnHW4{f~lN#rPc2LRID7sfpZZMvLE_SmI%IT7`yIeTTp%*J?ZkaHGv%+*ki2`Hv4nkn20~kn~0zEpX(Kw z>b4zQ7J9KDweF6=CT(LXPIZjSAYGs*3b(}uXYoV_{6P9*W(t&GgA;uF)fRQ=?7QF{ zT>l2{2VXVvD%01Id~)GZI^WUgz~AopN=t3NQu>a@?ac75aTznB5y*S+qNff#pTqJ` zs=%!XlQnXTIa{YSN9$^yM{0QL7=+6w$GCHL!jqp$v4jnrqy~&I=GzxN*rvk^RPmvM zSe&Hha*8WNess_JbWZI3f~OoM-eS_`rwt`EN*Y@D`~C;Aff|ki?O3!T1f}M`9nPYx zSi!rvBmAPv@|hb}jn5yz->wDQ!*tgHUQ`-G0GFF^bHXXevVvU>=#t1H?0{>CDP}8R z6fxB}3^1pb#ANY@pt0ML6-eGd6X2u`Rs5J&q_NuKF5EqIoW^<#y=lME%CrA* zwhv*)uQ4~A>R=7Hd++Q5;>nj?G&*g4UtJ68D%4r=dSNDR$WAw#7iH`HjyBGOeq1Cc ze;;G)O5!GlHJBuS;`=rP+@f{f0#(ar;@oh>tTyX=#KCUmk_jrL9X!4p5HNj0{xrGx zG3PXLR(gJKy>YW6v9~2y;VXsfHsv1vq-3vb-n8TIg~(w>@a8;?O&n4-e)i2+lM~f7 zYrKFVl`od$^p~KBNaLU%LZGd7eiZcvJm`lAXsd(&Ahr2=(6vtJ(}7|aMu8=xz@JfI z%qZ|=6u=n;!HlQZ8BZTGF4^Sz*WB6gil3T3-Po!c5b3scl(ZyWI-vc33F!1`REsL} zwcC~;X-TYf0BnP&zd`AuXqA5qg9pp--JZz0K_I2 zUUP@&6_1`iy$>uE?zSaFTKaDyxaJPmE1m-+fEmDti;$K;r2{aV-2WzcrcWPiQRRE> zCM-abJXWf7*2eLAP5Uda`2S5rxCslBBqd9guGu&~u4!-biVp#1w5sy`H*um=>5`43 zNlp8EulO6&r^A4y!rg=qlO%zJ0O{#yv5T<~V`%lB!q(}Z6)uw=c6c3&#UZ0)v z1ML>FYCI=9_JJ=OzDs*mhvEN)vhkm^^CPWU{C{E4pQF#&% keep(names(.) %in% output_kin) + } + + # as data.frame + kin <- map2(kin_list, names(kin_list), + function(x,y){ + # y <- "m" + # x <- kin_list[["m"]] + out <- as.data.frame(x) + colnames(out) <- age + out %>% + mutate(kin = y, + age_kin = sort(rep(age,s)), + stage_kin = rep(1:s,ages), + alive = rep("yes",s*ages)) %>% + pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% + mutate(age_focal = as.integer(age_focal)) + } + ) %>% + reduce(rbind) + + # results as list? + if(list_output) { + out <- kin_list + }else{ + out <- kin + } + + return(out) +} diff --git a/dev/multi_stage_tests.R b/dev/multi_stage_tests.R deleted file mode 100644 index 93e84a6..0000000 --- a/dev/multi_stage_tests.R +++ /dev/null @@ -1,115 +0,0 @@ -library(R.matlab) - -# Program: matrix_construction_4867.m -# This Matlab script reads in tables giving life tables and parity transitions for Slovakia (obtained from the Human Mortality and Human Fertility databases, respectively), and constructs the age-specific parity transition matrices, the parity-specific survival matrices, the age-specific fertility matrices, and the parity-specific offspring assignment matrices. - -matrices <- readMat("../../multi_stage_caswell/SVK_kinmats/SVKmats1952.mat") -names(matrices) - -# omega -matrices$om - -# stages -matrices$s - -# stage transition prob: list of w elements with s x s matrix -matrices$U - -# age advance (depends if U includes survival or not): list of s elements with w x w matrix -matrices$D - -# state-specific fertility: list of w elements with s x s matrix -matrices$F - -# assigns the offspring of individuals in stage j to the appropriate age class: list of s elements with w x w matrix -matrices$H - -# Program: calling_kinship_SVK_4867.m -# This Matlab script reads in set of matrices produced by the program matrix_construction_4867.m and then calls the function kinship_function_parity_4867.m to obtain the resulting kinship network. The results are saved in the folder SVK_kinout with filenames SVKkinout1960.mat, etc. - -years=1960:2014; - -numyears=length(years); #specific to SVK data -#add path to location of matrices -addpath('SVK_kinmats/') - -for (iy in 1:numyears){ - - iy = 1 - year=years[iy] - - #specify name of matrix file - matrixs <- readMat(paste0("../../multi_stage_caswell/SVK_kinmats/SVKmats",year,".mat")) - om = matrices$om - s = matrices$s - U = matrices$U - D = matrices$D - F = matrices$F - H = matrices$H - - #create the block diagonal matrices - - #identity matrices that are useful - Iom=diag(1,om, om); - Is=diag(1,s, s); - - bbU=matrix(0,s*om,s*om); - bbF=matrix(0,s*om,s*om); - for (i in 1:om){ - bbU = bbU + kron(Iom(:,i)*Iom(i,:),U{i}); #D is not useful - bbF = bbF + kron(Iom(:,i)*Iom(i,:),F{i}); - } - bbD=zeros(s*om); - bbH=zeros(s*om); - for (i in 1:s){ - bbD = bbD+kron(Is(:,i)*Is(i,:),D{i}); - bbH = bbH+kron(Is(:,i)*Is(i,:),H{i}); - } - - # create the age-stage matrices using the vec permuation formula - K=vecperm(s,om); - Ut= t(K)*bbD*K*bbU; - Ft= t(K)*bbH*K*bbF; - - #conditional transition matrix, conditional on survival - Gt=Ut*pinv(diag(sum(Ut))); - - #calculate distributions of mothers - #projection matrix Atilde - At=Ut+Ft; - #eigenvalues and right eigenvectors - [wt,d]=eig(At); - d=diag(d); - #find maximum eigenvalue - pick=find(d==max(d)); - wt=wt(:,pick); - #stable age-parity distribution normalized to sum to 1 - wt=wt/sum(wt); - lambda=d(pick) - - #age-stage distribution of mothers - pit=t(Ft(1,:)).*wt; - pit=pit/sum(pit); - # marginal age distribution of mothers - piage=kron(Iom,t(ones(s,1))*pit; - -#add path to call the kinship program -path('../',path) - -#call the kinship function -kinout=kinship_function_parity(Ut,Ft,Gt,wt,pit,piage); - -#save the kin output -#include path to output folder -myname=char(['SVK_kinout/SVKkinout' num2str(years(iy)) '.mat']) -save(myname,'kinout') - -} - - -# Program: kinship_function_parity_4867.m -# This Matlab function takes as input a set of block-structured transition and fertility matrices and returns the kinship results. - - - - diff --git a/tests/repl_caswell.R b/dev/tests/repl_caswell.R similarity index 99% rename from tests/repl_caswell.R rename to dev/tests/repl_caswell.R index 3b53057..b81c5ee 100644 --- a/tests/repl_caswell.R +++ b/dev/tests/repl_caswell.R @@ -9,6 +9,7 @@ library(R.matlab) load("tests/test.RData") # basic +debugonce(kin_time_variant) swe_kin_period_pack <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, diff --git a/tests/repl_caswell_first_year.R b/dev/tests/repl_caswell_first_year.R similarity index 100% rename from tests/repl_caswell_first_year.R rename to dev/tests/repl_caswell_first_year.R diff --git a/dev/tests/timevarying_kin.m b/dev/tests/timevarying_kin.m new file mode 100644 index 0000000..c1176c8 --- /dev/null +++ b/dev/tests/timevarying_kin.m @@ -0,0 +1,180 @@ +function kout=timevarying_kin(U,F,pi,om,pkin); +% function to return kinship network +% calculated from the rates and the kinship at the previous time +% U=survival matrix +% F=fertility matrix +% pi = distribution of ages of mothers +% om=number of age classes +% pkin = the array of all kin from the previous time step +% model structure +% k(x+1,t+1)=U(t)*k(x,t) + F(t)*kstar(x,t) for some other kin kstar + +%set to full in case they arrive as sparse matrices +U=full(U); +F=full(F); +pi=full(pi); + +%frequently used zero vector for initial condition +zvec=zeros(om,1); +I=eye(om); +omz=om-1; + +% a: daughters of focal + +A(:,1)=zvec; +for ix=1:omz + ap=U*pkin.A(:,ix) + F*I(:,ix); + A(:,ix+1)=ap; + +end % for ix + +% b = granddaughters of Focal + +B(:,1)=zvec; +for ix=1:omz + bp=U*pkin.B(:,ix) + F*pkin.A(:,ix); + B(:,ix+1)=bp; + +end + + +% c = greatgranddaughters of Focal +C(:,1)=zvec; +for ix=1:omz + cp=U*pkin.C(:,ix) +F*pkin.B(:,ix); + C(:,ix+1)=cp; + +end + + +% d = mothers of Focal +D(:,1)=pi; +for ix=1:omz + dp=U*pkin.D(:,ix) + 0; + D(:,ix+1)=dp; + +end + + +% g = grandmothers of Focal +%only maternal grandmothers right now +G(:,1)=pkin.D*pi;; +for ix=1:omz + gp=U*pkin.G(:,ix) + 0; + G(:,ix+1)=gp; + +end + + +% h = greattrandmothers of Focal + +H(:,1)=pkin.G*pi; +for ix=1:omz + hp=U*pkin.H(:,ix) + 0; + H(:,ix+1)=hp; + +end + +% m = older sisters of Focal + +M(:,1)=pkin.A*pi; +for ix=1:omz + mp=U*pkin.M(:,ix) + 0; + M(:,ix+1)=mp; + +end + +% n = younger sisters + +N(:,1)=zvec; +for ix=1:omz + np=U*pkin.N(:,ix) + F*pkin.D(:,ix); + N(:,ix+1)=np; + +end + + +% p = nieces through older sisters + +P(:,1)=pkin.B*pi; +for ix=1:omz + pp=U*pkin.P(:,ix) + F*pkin.M(:,ix); + P(:,ix+1)=pp; +end + +% q = nieces through younger sisters + +Q(:,1)=zvec; +for ix=1:omz + qp=U*pkin.Q(:,ix) + F*pkin.N(:,ix); + Q(:,ix+1)=qp; + +end + +% r = aunts older than mother + +R(:,1)=pkin.M*pi; +for ix=1:omz + rp=U*pkin.R(:,ix) + 0; + R(:,ix+1)=rp; + +end + +% s = aunts younger than mother + +S(:,1)=pkin.N*pi; +for ix=1:omz + sp=U*pkin.S(:,ix) + F*pkin.G(:,ix); + S(:,ix+1)=sp; + +end + +% t = cousins from older aunts + +T(:,1)=pkin.P*pi; +for ix=1:omz + tp=U*pkin.T(:,ix) + F*pkin.R(:,ix); + T(:,ix+1)=tp; + +end + + +% v = cousins from aunts younger than mother + +V(:,1)=pkin.Q*pi; +for ix=1:omz + vp=U*pkin.V(:,ix) + F*pkin.S(:,ix); + V(:,ix+1)=vp; + +end + +%concatenate kin matrices +allkin=cat(3,A,B,C,D,G,H,M,N,P,Q,R,S,T,V); + +%concatenate, combining older and younger sisters, etc. +allkin2=cat(3,A,B,C,D,G,H,M+N,P+Q,R+S,T+V); + +%create output structures +kout.A=A; +kout.B=B; +kout.C=C; +kout.D=D; +kout.G=G; +kout.H=H; +kout.M=M; +kout.N=N; +kout.P=P; +kout.Q=Q; +kout.R=R; +kout.S=S; +kout.T=T; +kout.V=V; + +kout.allkin=allkin; +kout.allkin2=allkin2; + +kout.U=U; +kout.F=F; +kout.pi=pi; + + \ No newline at end of file diff --git a/man/get_HMDHFD.Rd b/man/get_HMDHFD.Rd index c7020b5..6c2ee13 100644 --- a/man/get_HMDHFD.Rd +++ b/man/get_HMDHFD.Rd @@ -8,8 +8,11 @@ get_HMDHFD( country = "SWE", min_year = 1900, max_year = 2018, - user = NULL, - pass = NULL + user_HMD = NULL, + pass_HMD = NULL, + user_HFD = NULL, + pass_HFD = NULL, + OAG = 100 ) } \arguments{ @@ -19,14 +22,20 @@ get_HMDHFD( \item{max_year}{numeric. Latest year to get data.} -\item{user}{character. From HMD/HFD.} +\item{user_HMD}{character. From HMD/HFD.} + +\item{pass_HMD}{character. From HMD/HFD.} + +\item{user_HFD}{character. From HMD/HFD.} -\item{pass}{character. From HMD/HFD.} +\item{pass_HFD}{character. From HMD/HFD.} + +\item{user}{character. From HMD/HFD.} } \value{ -A list wiith survival, fertility and poopulation age specific matrixes, with calendar year as colnames. +A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames. } \description{ -Wrapper function to get data.frames of survival, fertlity and population +Wrapper function to get data.frames of female survival, fertlity and population of selected countriy on selected period. } diff --git a/man/kin.Rd b/man/kin.Rd index 4e1b7b8..52cf0a3 100644 --- a/man/kin.Rd +++ b/man/kin.Rd @@ -7,39 +7,36 @@ kin( U = NULL, f = NULL, + time_invariant = TRUE, N = NULL, pi = NULL, - stable = TRUE, - focal_cohort = NULL, - focal_year = NULL, - selected_kin = NULL, + output_cohort = NULL, + output_period = NULL, + output_kin = NULL, birth_female = 1/2.04, - Pb = FALSE, living = TRUE ) } \arguments{ -\item{U}{numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year.} +\item{U}{numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).} -\item{f}{numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year.} +\item{f}{numeric. Same as U but for fertility rates.} -\item{N}{numeric. A matrix of population with rows as ages and columns as years. The name of each col must be the year.} +\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Defautk \code{TRUE}.} -\item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year.} +\item{N}{numeric. Same as U but for population distribution (counts or \verb{\%}). Optional.} -\item{stable}{logical. Stable assumption given \code{year} rates, taking focal_year in case is U and f are matrix.} +\item{pi}{numeric. Same as U but for childbearing distribution (sum to 1). Optional.} -\item{focal_cohort}{integer. Year of birth of focal. Could be a vector. Should be within input data years range.} +\item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.} -\item{focal_year}{integer. Year of focal. Could be a vector. Should be within input data years range.} +\item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.} -\item{selected_kin}{character. kin to return: "m" for mother, "d" for daughter,...} +\item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...} \item{birth_female}{numeric. Female portion at birth.} -\item{Pb}{logic. Is given Pb as the first row in P?. If not, takes \code{P(0,1)} as \code{P(b,1)}. Useful for fertility matrix first row. Default \code{FALSE}.} - -\item{living}{logial. Only living kin counts \code{TRUE}, or including death kin \code{FALSE}.} +\item{living}{logical. Only living kin counts \code{TRUE}, or including death kin \code{FALSE}.} } \value{ A list with: @@ -57,18 +54,3 @@ Implementation of Goodman-Keyfitz-Pullum equations in a stable or not framework. \details{ See Caswell (2019) and Caswell (2021) for details on formulas. } -\examples{ -# If focal is 30 years old and lives in 1950. How much live kin would have if -# her relatives would experience mortality and fertility in that calendar year? -\dontrun{ -swe30_1950_stable <- kin(U = swe_surv, f = swe_asfr, focal_year = 1950, stable = TRUE,selected_kin = c("m","gm")) -# How much live mothers and grandmothers would have if her relatives would experience -# mortality and fertility at each observed year? -swe30_1950_nonstable <- kin(U = swe_surv, f = swe_asfr,N = swe_pop, - stable = FALSE, focal_year = 1950, selected_kin = c("m","gm")) -# Difference in total by kin: -swe30_1950_stable$kin_by_age_focal \%>\% filter(age_focal==30) \%>\% select(kin, total) -swe30_1950_nonstable$kin_by_age_focal \%>\% filter(age_focal==30) \%>\% select(kin, total) -} - -} diff --git a/man/kin_non_stable.Rd b/man/kin_non_stable.Rd deleted file mode 100644 index 647793a..0000000 --- a/man/kin_non_stable.Rd +++ /dev/null @@ -1,44 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/kin_non_stable.R -\name{kin_non_stable} -\alias{kin_non_stable} -\title{Estimate kin counts in a non stable framework} -\usage{ -kin_non_stable( - U = NULL, - f = NULL, - N = NULL, - pi = NULL, - focal_cohort = NULL, - focal_year = NULL, - selected_kin = NULL, - birth_female = 1/2.04, - Pb = FALSE -) -} -\arguments{ -\item{U}{numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year.} - -\item{f}{numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year.} - -\item{N}{numeric. A matrix of population with rows as ages and columns as years. The name of each col must be the year.} - -\item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year.} - -\item{focal_cohort}{integer. Year of birth of focal. Could be a vector. Should be within input data years range.} - -\item{focal_year}{integer. Year of focal. Could be a vector. Should be within input data years range.} - -\item{selected_kin}{character. kin to return: "m" for mother, "d" for daughter,...} - -\item{birth_female}{numeric. Female portion at birth.} - -\item{Pb}{logic. Is given Pb as the first row in P?. If not, takes \code{P(0,1)} as \code{P(b,1)}. Useful for fertility matrix first row. Default \code{FALSE}.} -} -\value{ -A data frame with focal´s age, related ages and type of kin -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. -} -\description{ -Implementation of non-stable Goodman-Keyfitz-Pullum equations based on Caswell (2021). -} diff --git a/man/kin_stable.Rd b/man/kin_stable.Rd deleted file mode 100644 index 1346437..0000000 --- a/man/kin_stable.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/kin_stable.R -\name{kin_stable} -\alias{kin_stable} -\title{Estimate kin counts in a stable framework} -\usage{ -kin_stable( - U = NULL, - f = NULL, - birth_female = 1/2.04, - pi = NULL, - selected_kin = NULL, - pi_stable = FALSE, - list_output = FALSE -) -} -\arguments{ -\item{U}{numeric. A vector of survival ratios.} - -\item{f}{numeric. A vector of age-specific fertility rates.} - -\item{birth_female}{numeric. Female portion at birth.} - -\item{pi}{numeric. For using the non-stable distribution of childbearing. Default \code{NULL}.} - -\item{selected_kin}{character. kin to return: "m" for mother, "d" for daughter,...} - -\item{pi_stable}{logical. Want mean age at childbearing as a result too. Default \code{FALSE}} - -\item{list_output}{logical. Want kin results as a list. Default \code{FALSE}} -} -\value{ -A data frame with focal´s age, related ages and type of kin -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. -} -\description{ -Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). -} diff --git a/man/kin_time_invariant.Rd b/man/kin_time_invariant.Rd new file mode 100644 index 0000000..a40fa34 --- /dev/null +++ b/man/kin_time_invariant.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kin_time_invariant.R +\name{kin_time_invariant} +\alias{kin_time_invariant} +\title{Estimate kin counts in a time invariant framework} +\usage{ +kin_time_invariant( + U = NULL, + f = NULL, + birth_female = 1/2.04, + pi = NULL, + output_kin = NULL, + living = TRUE, + list_output = FALSE +) +} +\arguments{ +\item{U}{numeric. A vector of survival ratios with same length as ages.} + +\item{f}{numeric. A vector of age-specific fertility rates with same length as ages.} + +\item{birth_female}{numeric. Female portion at birth.} + +\item{pi}{numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default \code{NULL}.} + +\item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.} + +\item{living}{logical. Only living kin counts \code{TRUE}, or including death kin \code{FALSE}.} + +\item{list_output}{logical. Results as a list. Default \code{FALSE}} +} +\value{ +A data frame with focal´s age, related ages and type of kin +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. +} +\description{ +Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). +} diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd new file mode 100644 index 0000000..2acfe54 --- /dev/null +++ b/man/kin_time_variant.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kin_time_variant.R +\name{kin_time_variant} +\alias{kin_time_variant} +\title{Estimate kin counts in a time variant framework} +\usage{ +kin_time_variant( + U = NULL, + f = NULL, + N = NULL, + pi = NULL, + output_cohort = NULL, + output_period = NULL, + output_kin = NULL, + birth_female = 1/2.04 +) +} +\arguments{ +\item{U}{numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.} + +\item{f}{numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. Coincident with \code{U}.} + +\item{N}{numeric. A matrix of population with rows as ages and columns as years. Coincident with \code{U}.} + +\item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with \code{U}.} + +\item{output_cohort}{integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range.} + +\item{output_period}{integer. Year for which to return kinship structure. Could be a vector. Should be within input data years range.} + +\item{output_kin}{character. kin to return as output: "m" for mother, "d" for daughter,... See \code{vignette} for exahustive kin.} + +\item{birth_female}{numeric. Female portion at birth.} +} +\value{ +A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), and age of (living or deceased) relatives. +} +\description{ +Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021). +} diff --git a/man/swe_surv.Rd b/man/swe_Sx.Rd similarity index 89% rename from man/swe_surv.Rd rename to man/swe_Sx.Rd index 73f272d..72f9da6 100644 --- a/man/swe_surv.Rd +++ b/man/swe_Sx.Rd @@ -1,8 +1,8 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{swe_surv} -\alias{swe_surv} +\name{swe_Sx} +\alias{swe_Sx} \title{Female swedish survival ratios from 1900 to 2015} \format{ A matrix with years as cols and ages (0 to 100 as OAG) as rows. @@ -11,7 +11,7 @@ A matrix with years as cols and ages (0 to 100 as OAG) as rows. HMD/HFD } \usage{ -swe_surv +swe_Sx } \description{ Female swedish survival ratios from 1900 to 2015 diff --git a/man/swe_px.Rd b/man/swe_px.Rd new file mode 100644 index 0000000..8e7e6c8 --- /dev/null +++ b/man/swe_px.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{swe_px} +\alias{swe_px} +\title{Female swedish survival probabilities from 1900 to 2015} +\format{ +A matrix with years as cols and ages (0 to 100 as OAG) as rows. +} +\source{ +HMD/HFD +} +\usage{ +swe_px +} +\description{ +Female swedish survival probabilities from 1900 to 2015 +} +\keyword{datasets} diff --git a/man/timevarying_kin.Rd b/man/timevarying_kin.Rd index dad6fff..66e8b5d 100644 --- a/man/timevarying_kin.Rd +++ b/man/timevarying_kin.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/kin_non_stable.R +% Please edit documentation in R/kin_time_variant.R \name{timevarying_kin} \alias{timevarying_kin} \title{one time projection kin} diff --git a/vignettes/Use.Rmd b/vignettes/Use.Rmd index 2d50bc4..2ff429a 100644 --- a/vignettes/Use.Rmd +++ b/vignettes/Use.Rmd @@ -15,18 +15,17 @@ library(tidyverse) library(DiagrammeR) library(knitr) library(progress) -library(igraph) ``` -The year is 2015 and focal is 35 years old. Focal and her relatives have all lived in Sweden. +The year is 2015 and focal is 35 years old^[Age should be understood as a class state, defined in a matrix transition framework.]. Focal and her relatives have all lived in Sweden. We would like to know how many female relatives focal has in 2015, their mean age and the number of living kin at each age of focal. Finally, we would like to know at which age she had to suffer a death in average. ## Kin counts with time invariant rates -We can estimate this in a **stable** framework using data from 2015, assuming that all of focal's relatives experience mortality and fertility from that calendar year (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example (*swe_surv*, *swe_asfr* and *swe_pop*, matrix of age by year, with survival, fertility and population; type `data(package="DemoKin")`). This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/). These datasets were loaded using the`get_HMDHFD` function. +We can estimate this in a **stable** framework using data from 2015, assuming that all of focal's relatives experience mortality and fertility from that calendar year (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example (*swe_px*, *swe_Sx*, *swe_asfr* and *swe_pop*, matrix of age by year, with survival probability, survival ratio, fertility and population; type `data(package="DemoKin")`). This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/). These datasets were loaded using the`get_HMDHFD` function. ```{r} -swe_surv_2015 <- swe_surv[,"2015"] +swe_surv_2015 <- swe_px[,"2015"] swe_asfr_2015 <- swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` @@ -76,11 +75,9 @@ swe_2015[["kin_full"]] %>% facet_wrap(~kin) ``` -The function assumes that fertility input: -- is for both sex. In case is only for one you need to set `birth_female = 1`. -- considers *effective* births. This is important in the case you are using survival rates ($S_x$) instead of probabilities ($p_x$). In the case your are using survival and you do not want all new born survives to truncated age 0 (it means $P_b=1$ in life table notation), fertility rates must consider this risk already. This should be done for example in the previous case, using as input `swe_asfr * $P_b$` for fertility. In the case of probabilities newborn would have 0 exact age, and no additional risk must be considered, +The function assumes that fertility input is for both sex, so in case this is only for one you need to set `birth_female = 1` in a convenient way. In the case you are using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should consider the adaptation for person-year exposure on females: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$ (Preston et.al, 2002). -The function also includes a summary output with the count of living kin, mean and standard devaition of kin age, by type of kin, for each focal's age: +The `kin` function also includes a summary output with the count of living kin, mean and standard deviation of kin age, by type of kin, for each focal's age: ```{r, fig.height=6, fig.width=8} swe_2015[["kin_summary"]] %>% @@ -90,7 +87,7 @@ swe_2015[["kin_summary"]] %>% kable() ``` -We can visualize the estimated kin counts by kin using a network diagram. Folloing with the age 35: +We can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35: ```{r} swe_2015_age35 <- swe_2015[["kin_summary"]] %>% @@ -100,14 +97,13 @@ plot_diagram(swe_2015_age35) ``` - ## Kin counts with time varying rates The demographic risks of population of Sweden is, in reality, changin every year, and focal relatives have been expossed to changing mortality and fertility rates. -To account for this, is needed estimations of kin counts in population with time-variant demographic rates. Let's take a look at the resulting kin counts for 1960 cohort: +To account for this, is needed estimations of kin counts in population with time-variant demographic rates. Let's take a look at the resulting kin counts for 1960 cohort, for some specific types: ```{r, fig.height=6, fig.width=8} -swe_2015_time_varying <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, time_invariant =FALSE, +swe_2015_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant =FALSE, output_cohort = 1960, output_kin = c("d","gd","ggd","m","gm","ggm")) @@ -129,7 +125,7 @@ As you see the estimates stop at age 55, because no data is after 2015. Kin loss has studied consequences related to mental health, care support and household incomes over life course. Setting the parameter `alive="no"` we can see for a focal from 1900 cohort how many relatives loss during her life (output `kin_death_by_age_focal`): ```{r, fig.height=6, fig.width=8} -swe_1900_time_varying <- kin(U = swe_surv, f = swe_asfr, N = swe_pop, time_invariant = FALSE, +swe_1900_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant = FALSE, output_cohort = 1900, output_kin = c("d","gd","ggd","m","gm","ggm"), living = FALSE) @@ -152,8 +148,38 @@ swe_1900_time_varying$kin_summary %>% kable() ``` +## Prevalences + +Once you have a full kin distribution by age, is possible to know, for example given a set of prevalence by age, what is the expected portion of living kin in each prevalence state (could be disease, employment, etc.). + +```{r} +# let´s create some prevalence shcedule by age +swe_2015_prevalence <- tibble(age_kin = unique(swe_2015$kin_full$age_kin), + prev = .005 * exp(.05 * age_kin)) + +plot(swe_2015_prevalence) + +# join to kin count estimates and plot +swe_2015$kin_full %>% + filter(alive=="yes") %>% + left_join(swe_2015_prevalence) %>% + group_by(kin, age_focal) %>% + summarise(prevalent = sum(count * prev), + no_prevalent = sum(count * (1-prev))) %>% + pivot_longer(cols = prevalent:no_prevalent, names_to = "prevalence_state", values_to = "count") %>% + ggplot(aes(x=age_focal, y = count)) + + geom_area(aes(fill=prevalence_state)) + + facet_wrap(~kin) + + theme_bw() + +``` + +This can be applied for time variant cases too. + ## References Caswell, H. (2019). The formal demography of kinhip: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24. Caswell, H., & Song, X. (2021). The formal demography of kinhip III: kinhip dynamics with time-varying demographic rates. Demographic Research, 45, 517–546. + +Preston, S., Heuveline, P., & Guillot, M. (2000). Demography: Measuring and Modeling Population Processes. Wiley. From 75409a75207f9dd9a06166d3751a776a7f6bbb2c Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Sun, 28 Aug 2022 10:49:26 -0300 Subject: [PATCH 05/21] multi_stage, improve imports --- DESCRIPTION | 6 +- NAMESPACE | 1 + R/data.R | 55 ++++++ R/kin.R | 75 ++++---- R/kin_multi_stage.R | 184 +++++++++++++++++++ R/kin_time_invariant.R | 29 ++- R/kin_time_variant.R | 98 +++++++---- R/plot_diagramm.R | 2 +- README.Rmd | 2 +- README.md | 2 +- data/kin_svk1990_caswell2020.rda | Bin 0 -> 875611 bytes data/svk_Hxs.rda | Bin 0 -> 255 bytes data/svk_Uxs.rda | Bin 0 -> 2870 bytes data/svk_fxs.rda | Bin 0 -> 1410 bytes data/svk_pxs.rda | Bin 0 -> 1046 bytes dev/kin_multi_stage.R | 223 ------------------------ man/kin.Rd | 24 +-- man/kin_multi_stage.Rd | 36 ++++ man/kin_svk1990_caswell2020.Rd | 19 ++ man/kin_time_invariant.Rd | 9 +- man/kin_time_variant.Rd | 7 +- man/output_period_cohort_combination.Rd | 16 ++ man/svk_Hxs.Rd | 19 ++ man/svk_Uxs.Rd | 19 ++ man/svk_fxs.Rd | 19 ++ man/svk_pxs.Rd | 19 ++ man/timevarying_kin.Rd | 8 +- tests/testthat.R | 12 ++ tests/testthat/test-kin_multi_stage.R | 20 +++ vignettes/Use.Rmd | 69 ++++++-- 30 files changed, 623 insertions(+), 350 deletions(-) create mode 100644 R/kin_multi_stage.R create mode 100644 data/kin_svk1990_caswell2020.rda create mode 100644 data/svk_Hxs.rda create mode 100644 data/svk_Uxs.rda create mode 100644 data/svk_fxs.rda create mode 100644 data/svk_pxs.rda delete mode 100644 dev/kin_multi_stage.R create mode 100644 man/kin_multi_stage.Rd create mode 100644 man/kin_svk1990_caswell2020.Rd create mode 100644 man/output_period_cohort_combination.Rd create mode 100644 man/svk_Hxs.Rd create mode 100644 man/svk_Uxs.Rd create mode 100644 man/svk_fxs.Rd create mode 100644 man/svk_pxs.Rd create mode 100644 tests/testthat.R create mode 100644 tests/testthat/test-kin_multi_stage.R diff --git a/DESCRIPTION b/DESCRIPTION index c1d035e..4ba1dc3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -13,9 +13,11 @@ Roxygen: list(markdown = TRUE) RoxygenNote: 7.2.1 Suggests: knitr, - rmarkdown + rmarkdown, + testthat (>= 3.0.0) VignetteBuilder: knitr -Imports: tidyverse, DiagrammeR, HMDHFDplus, devtools, progress +Imports: dplyr, tidyr, purrr, DiagrammeR, HMDHFDplus, devtools, progress, matrixcalc, Matrix, MASS, ggplot2, stats BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) +Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index d0806d3..3ac5760 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,6 +2,7 @@ export(get_HMDHFD) export(kin) +export(kin_multi_stage) export(kin_time_invariant) export(kin_time_variant) export(plot_diagram) diff --git a/R/data.R b/R/data.R index 2fd5f27..1bacb90 100644 --- a/R/data.R +++ b/R/data.R @@ -72,3 +72,58 @@ #' @source #' Caswell (2019) "pi_caswell_2021" + +#' Female Slovakian survival probabilities by parity stage in 1990 (Caswell, 2021) +#' +#' Female Slovakian survival probabilities by parity stage in 1990 (Caswell, 2021) +#' @docType data +#' @format +#' A matrix of px with stages as cols and ages as rows. +#' +#' @source +#' Caswell (2021) +"svk_pxs" + +#' Female Slovakian fertility rates by parity stage in 1990 (Caswell, 2021) +#' +#' Female Slovakian fertility rates by parity stage in 1990 (Caswell, 2021) +#' @docType data +#' @format +#' A matrix of fx with stages as cols and ages as rows. +#' +#' @source +#' Caswell (2021) +"svk_fxs" + +#' Age where assign offspring of individuals in each partity stage (Caswell, 2021). All to zero age in this case. +#' +#' Age where assign offspring of individuals in each partity stage (Caswell, 2021). All to zero age in this case. +#' @docType data +#' @format +#' A matrix of ones in ages where assign offspring individuals, with stages as cols and ages as rows. +#' +#' @source +#' Caswell (2021) +"svk_Hxs" + +#' Probability of transition among parity stage for Slovakia in 1990, for each age, conditional on survival (Caswell, 2021). +#' +#' Probability of transition among parity stage for Slovakia in 1990, for each age, conditional on survival (Caswell, 2021). +#' @docType data +#' @format +#' A list of column-stochastic matrix with probabilities of transition among parity stage, for each age, conditional on survival. +#' +#' @source +#' Caswell (2021) +"svk_Uxs" + +#' Output for Slovakia 1990 in Caswell (2020). +#' +#' Output for Slovakia 1990 in Caswell (2020). +#' @docType data +#' @format +#' A list with specific kin types age-stage matrix +#' +#' @source +#' Caswell (2021) +"kin_svk1990_caswell2020" diff --git a/R/kin.R b/R/kin.R index a111d9d..4c14cc3 100644 --- a/R/kin.R +++ b/R/kin.R @@ -1,23 +1,25 @@ #' Estimate kin counts -#' @description Implementation of Goodman-Keyfitz-Pullum equations in a stable or not framework. +#' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. #' @details See Caswell (2019) and Caswell (2021) for details on formulas. #' @param U numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). #' @param f numeric. Same as U but for fertility rates. -#' @param time_invariant logical. Constant assumption for a given `year` rates. Defautk `TRUE`. +#' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`. #' @param N numeric. Same as U but for population distribution (counts or `%`). Optional. #' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional. #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range. #' @param output_period integer. Vector of period years for returning results. Should be within input data years range. #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,... #' @param birth_female numeric. Female portion at birth. -#' @param living logical. Only living kin counts `TRUE`, or including death kin `FALSE`. #' @return A list with: -#' * `kin`: a data frame with focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.). The content (living or deaths) dependes on `alive` param. -#' * A data frame with kin at each age of focal´s life. The content (living or deaths) dependes on `alive` param. -#' * A data frame with kin at actual age of focal. The content (living or deaths) dependes on `alive` param. -#' * Mean age of each type of kin at actual age of focal. The content (living or deaths) dependes on `alive` param. -#' * Total of each type of kin at actual age of focal. The content (living or deaths) dependes on `alive` param. +#' * `kin_full`: a data frame with focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age. +#' * `kin_summary`: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`: +#' - `count`: count of living kin at actual age of focal +#' - `mean_age`: mean age of each type of living kin. +#' - `sd_age`: standard deviation age of each type of living kin . +#' - `count_death`: count of death kin at specific age of focal. +#' - `count_cum_death`: cumulated count of death kin at specific age of focal. +#' - `mean_age_lost`: mean age where focal lost her relative. #' @export #' # get kin ---------------------------------------------------------------- @@ -25,8 +27,7 @@ kin <- function(U = NULL, f = NULL, time_invariant = TRUE, N = NULL, pi = NULL, output_cohort = NULL, output_period = NULL, output_kin=NULL, - birth_female = 1/2.04, - living = TRUE) + birth_female = 1/2.04) { age <- as.integer(rownames(U)) @@ -49,7 +50,7 @@ kin <- function(U = NULL, f = NULL, } kin_full <- kin_time_invariant(U = U, f = f, output_kin = output_kin, birth_female = birth_female) %>% - mutate(cohort = NA, year = NA) + dplyr::mutate(cohort = NA, year = NA) }else{ kin_full <- kin_time_variant(U = U, f = f, N = N, pi = pi, output_cohort = output_cohort, output_period = output_period, @@ -59,30 +60,34 @@ kin <- function(U = NULL, f = NULL, } # reorder - kin_full <- kin_full %>% select(year, cohort, age_focal, kin, age_kin, alive, count) + kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, alive, count) - # return - kin_summary <- kin_full %>% - filter(alive=="yes") %>% - rename(total=count) %>% - group_by(year, cohort, age_focal, kin) %>% - summarise(count = sum(total), - mean_age = sum(total*age_kin)/sum(total), - sd_age = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>% - ungroup() - if(living){ - kin_out <- list(kin_full=kin_full %>% filter(alive=="yes"), kin_summary=kin_summary) - }else{ - kin_summary <- kin_full %>% - rename(total=count) %>% - group_by(cohort, age_focal, kin, alive) %>% - summarise(count = sum(total)) %>% - ungroup() %>% - group_by(cohort, kin, alive) %>% - mutate(count_cum = cumsum(count), - mean_age_lost = cumsum(count*age_focal)/cumsum(count)) %>% - ungroup() - kin_out <- list(kin_full=kin_full, kin_summary=kin_summary) - } + # summary + kin_summary <- dplyr::bind_rows( + kin_full %>% + dplyr::filter(alive=="yes") %>% + dplyr::rename(total=count) %>% + dplyr::group_by(cohort, alive, age_focal, kin) %>% + dplyr::summarise(count = sum(total), + mean_age = sum(total*age_kin)/sum(total), + sd_age = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>% + tidyr::pivot_longer(count:sd_age, names_to = "indicator", "value"), + kin_full %>% + dplyr::filter(alive=="no") %>% + dplyr::rename(total=count) %>% + dplyr::group_by(cohort, alive, age_focal, kin) %>% + dplyr::summarise(count_death = sum(total)) %>% + dplyr::group_by(cohort, alive, kin) %>% + dplyr::mutate(count_cum_death = cumsum(count_death), + mean_age_lost = cumsum(count_death*age_focal)/cumsum(count_death)) %>% + dplyr::ungroup() %>% + tidyr::pivot_longer(count_death:mean_age_lost, names_to = "indicator", "value") + ) %>% + dplyr::ungroup() %>% + dplyr::select(cohort, age_focal, kin, indicator, value) %>% + tidyr::pivot_wider(names_from = indicator, values_from = value) + + # return + kin_out <- list(kin_full = kin_full, kin_summary = kin_summary) return(kin_out) } diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R new file mode 100644 index 0000000..fa70665 --- /dev/null +++ b/R/kin_multi_stage.R @@ -0,0 +1,184 @@ +#' Estimate kin counts by age and stage in a time invariant framework + +#' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). Stages are implied in length of input lists. + +#' @param U list. age elemnts with column-stochastic transition matrix with dimension for the state space, conditional on survival. +#' @param f matrix. state-specific fertility (age in rows and states in columns). +#' @param D matrix. survival probabilities by state (age in rows and states in columns) +#' @param H matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns). +#' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. +#' @param list_output logical. Results as a list. Default `FALSE`. + +#' @return A data frame with focal´s age, related ages and type of kin +#' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death, and specific stage. If `list_output = TRUE` then this is a list. +#' @export +#' + +kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, + birth_female = 1, + output_kin = NULL, + list_output = FALSE){ + + if(!is.list(U)) stop("U must be a list with age length of elements, and stage transitiotn matrix for each one.") + + # stages and ages + s <- ncol(U[[1]]) + ages <- length(U) + age <- (1:ages)-1 + + # build matrix structure from data.frame input + H <- purrr::map(colnames(D), function(Y){ + Ht = matrix(0, nrow=ages, ncol=ages) + Ht[1,] <- 1 + Ht + }) + D <- purrr::map(colnames(D), function(Y){ + X <- D[,Y] + Dt = matrix(0, nrow=ages, ncol=ages) + Dt[row(Dt)-1 == col(Dt)] <- X[-ages] + Dt[ages, ages] = X[ages] + Dt + }) + f <- purrr::map(1:ages, function(Y){ + X <- f[Y,] + ft = matrix(0, nrow=s, ncol=s) + ft[1,] <- X + ft + }) + + # build block matrix + bbU <- Matrix::bdiag(U) + bbF <- Matrix::bdiag(f) * birth_female + bbD <- Matrix::bdiag(D) + bbH <- Matrix::bdiag(H) + + # rearrange with conmutation matrix + K <- matrixcalc::commutation.matrix(s, ages) + Ut <- t(K) %*% bbD %*% K %*% bbU + ft <- t(K) %*% bbH %*% K %*% bbF + Gt <- Ut%*% MASS::ginv(diag(colSums(as.matrix(Ut)))) + + # stable distribution mothers: age x stage + At <- Ut + ft + A_decomp <- eigen(At) + lambda <- as.double(A_decomp$values[1]) + wt <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1])) + pi <- wt*At[1,]/sum(wt*At[1,]) + + # marginal mothers age + Iom <- diag(1,ages, ages); + ones <- t(rep(1,s)) + onesom <- t(rep(1,s*ages)) + piage <- kronecker(Iom,ones) %*% pi + + # momarray is an array with pit in each column + momarray <- pi %*% matrix(1,1,ages) + Iom = diag(1, ages) + Is = diag(1, s) + Isom = diag(1, s*ages) + zsom = matrix(0, s*ages, s*ages) + Z=Is; + Z[1,1]=0; + for(i in 1:ages){ + # imom = 1 + E <- Iom[,i] %*% t(Iom[i,]); # al cuadrado? + momarray[,i] <- kronecker(E,Z) %*% momarray[,i] + } + # re-scale + momarray <- momarray %*% MASS::ginv(diag(colSums(momarray))) + + # considering deaths + phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*s*2,ages) + phi[1,1] = 1 + Mtt <- diag(as.numeric(onesom - onesom %*% Ut)) + Utt <- rbind(cbind(Ut,zsom), cbind(Mtt,Isom)) %>% as.matrix() + ftt <- rbind(cbind(ft,zsom), cbind(zsom,zsom)) %>% as.matrix() + Gtt <- rbind(cbind(Gt,zsom), cbind(zsom,zsom)) %>% as.matrix() + sages <- 1:(ages*s) + + # no considering deaths + # phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*s,ages) + # phi[1,1] = 1 + # Utt = Ut %>% as.matrix() + # ftt = ft %>% as.matrix() + # Gtt = Gt %>% as.matrix() + + # focal´s trip + m[sages,1] = momarray %*% piage; + for(i in 1:(ages-1)){ + phi[,i+1] = Gtt %*% phi[,i] + d[,i+1] = Utt %*% d[,i] + ftt %*% phi[,i] + gd[,i+1] = Utt %*% gd[,i] + ftt %*% d[,i] + ggd[,i+1] = Utt %*% ggd[,i] + ftt %*% gd[,i] + m[,i+1] = Utt %*% m[,i] + ys[,i+1] = Utt %*% ys[,i] + ftt %*% m[,i] + nys[,i+1] = Utt %*% nys[,i] + ftt %*% ys[,i] + } + + gm[,1] = m %*% piage + for(i in 1:(ages-1)){ + gm[,i+1] = Utt %*% gm[,i] + } + + ggm[,1] = gm %*% piage + for(i in 1:(ages-1)){ + ggm[,i+1] = Utt %*% ggm[,i] + } + + os[,1] = d %*% piage + nos[,1] = gd[1:ages,] %*% piage + for(i in 1:(ages-1)){ + os[,i+1] = Utt %*% os[,i] + nos[,i+1] = Utt %*% nos[,i] + ftt %*% os[,i] + } + + oa[,1] = os %*% piage + ya[,1] = ys %*% piage + coa[,1] = nos %*% piage + cya[,1] = nys %*% piage + for(i in 1:(ages-1)){ + oa[,i+1] = Utt %*% oa[,i] + ya[,i+1] = Utt %*% ya[,i] + ftt %*% gm[,i] + coa[,i+1] = Utt %*% coa[,i] + ftt %*% oa[,i] + cya[,i+1] = Utt %*% cya[,i] + ftt %*% ya[,i] + } + + # get results + kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys, + nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya) + + # only selected kin + if(!is.null(output_kin)){ + kin_list <- kin_list %>% keep(names(.) %in% output_kin) + } + + # as data.frame + kin <- purrr::map2(kin_list, names(kin_list), + function(x,y){ + out <- as.data.frame(x) + colnames(out) <- age + out %>% + dplyr::mutate(kin = y, + age_kin = rep(sort(rep(age,s)),2), + stage_kin = rep(rep(1:s,ages),2), + alive = c(rep("yes",s*ages),rep("no",s*ages)) + # age_kin = sort(rep(age,s)), + # stage_kin = rep(1:s,ages), + # alive = c(rep("yes",s*ages)) + ) %>% + tidyr::pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% + dplyr::mutate(age_focal = as.integer(age_focal)) + }) %>% + purrr::reduce(rbind) + + # results as list? + if(list_output) { + out <- kin_list + }else{ + out <- kin + } + + return(out) +} + + diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R index 18ed634..b390268 100644 --- a/R/kin_time_invariant.R +++ b/R/kin_time_invariant.R @@ -2,23 +2,21 @@ #' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). -#' @param U numeric. A vector of survival ratios with same length as ages. +#' @param U numeric. A vector of survival probabilities with same length as ages. #' @param f numeric. A vector of age-specific fertility rates with same length as ages. #' @param birth_female numeric. Female portion at birth. #' @param pi numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default `NULL`. #' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. -#' @param living logical. Only living kin counts `TRUE`, or including death kin `FALSE`. -#' @param list_output logical. Results as a list. Default `FALSE` +#' @param list_output logical. Results as a list with `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE` #' #' @return A data frame with focal´s age, related ages and type of kin -#' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. +#' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. If `list_output = TRUE` then this is a list. #' @export kin_time_invariant <- function(U = NULL, f = NULL, birth_female = 1/2.04, pi = NULL, output_kin = NULL, - living = TRUE, list_output = FALSE){ # make matrix transition from vectors @@ -94,29 +92,24 @@ kin_time_invariant <- function(U = NULL, f = NULL, # only selected kin if(!is.null(output_kin)){ - kin_list <- kin_list %>% keep(names(.) %in% output_kin) + kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin) } # as data.frame - kin <- map2(kin_list, names(kin_list), + kin <- purrr::map2(kin_list, names(kin_list), function(x,y){ out <- as.data.frame(x) colnames(out) <- age out %>% - mutate(kin = y, - age_kin = rep(age,2), - alive = c(rep("yes",ages), rep("no",ages))) %>% - pivot_longer(c(-age_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% - mutate(age_focal = as.integer(age_focal)) + dplyr::mutate(kin = y, + age_kin = rep(age,2), + alive = c(rep("yes",ages), rep("no",ages))) %>% + tidyr::pivot_longer(c(-age_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% + dplyr::mutate(age_focal = as.integer(age_focal)) } ) %>% - reduce(rbind) + purrr::reduce(rbind) - # only living kin? - if(living) { - kin <- kin %>% filter(alive == "yes") - kin_list <- map(kin_list, function(x) {x[1:ages,]}) - } # results as list? if(list_output) { diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index 09465cc..4fc8c4d 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -10,14 +10,15 @@ #' @param output_period integer. Year for which to return kinship structure. Could be a vector. Should be within input data years range. #' @param output_kin character. kin to return as output: "m" for mother, "d" for daughter,... See `vignette` for exahustive kin. #' @param birth_female numeric. Female portion at birth. -#' +#' @param list_output logical. Results as a list with years elements (as a result of `output_cohort` and `output_period` combination), with a second list of `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE` + #' @return A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives -#' (for example `d` is daughter, `oa` is older aunts, etc.), and age of (living or deceased) relatives. +#' (for example `d` is daughter, `oa` is older aunts, etc.), and age of (living or deceased) relatives. If `list_output = TRUE` then this is a list. #' @export kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, output_cohort = NULL, output_period = NULL, output_kin = NULL, - birth_female = 1/2.04){ + birth_female = 1/2.04, list_output = FALSE){ # check input if(is.null(U) | is.null(f)) stop("You need values on U and/or f.") @@ -64,7 +65,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, # loop over years (more performance here) kin_all <- list() - pb <- progress_bar$new( + pb <- progress::progress_bar$new( format = "Running over input years [:bar] :percent", total = n_years_data, clear = FALSE, width = 60) for (iyear in 1:n_years_data){ @@ -84,7 +85,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, f1 <- ft[1,][1:ages] pi1 <- pit[1:ages] kin_all[[1]] <- kin_time_invariant(U = U1, f = f1, pi = pi1, birth_female = birth_female, - list_output = TRUE, living = FALSE) + list_output = TRUE) } kin_all[[iyear+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[iyear]]) pb$tick() @@ -92,18 +93,10 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, # filter years and kin that were selected names(kin_all) <- as.character(years_data) - if(!is.null(output_cohort)){ - selected_cohorts_year_age <- data.frame(age = rep(age,length(output_cohort)), - year = map(output_cohort,.f = ~.x+age) %>% - unlist(use.names = F)) - }else{selected_cohorts_year_age <- c()} - if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% rename(age=1,year=2) - }else{selected_years_age <- c()} - if(is.null(output_period) & is.null(output_cohort)){ - message("No specific output was set. Return all period data.") - output_period <- years_data - } - out_selected <- bind_rows(selected_years_age,selected_cohorts_year_age) %>% distinct() + + # combinations to return + out_selected <- output_period_cohort_combination(output_cohort, output_period, age = age, years_data = years_data) + possible_kin <- c("d","gd","ggd","m","gm","ggm","os","ys","nos","nys","oa","ya","coa","cya") if(is.null(output_kin)){ selected_kin_position <- 1:length(possible_kin) @@ -112,28 +105,36 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, } # first filter - kin_all <- kin_all %>% - keep(names(.) %in% as.character(unique(out_selected$year))) %>% - map(~ .[selected_kin_position]) + kin_list <- kin_all %>% + purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>% + purrr::map(~ .[selected_kin_position]) # long format - kin <- lapply(names(kin_all), function(Y){ - X <- kin_all[[Y]] - X <- map2(X, names(X), function(x,y) as.data.frame(x) %>% - mutate(year = Y, + kin <- lapply(names(kin_list), function(Y){ + X <- kin_list[[Y]] + X <- purrr::map2(X, names(X), function(x,y) as.data.frame(x) %>% + dplyr::mutate(year = Y, kin=y, age_kin = rep(age,2), alive = c(rep("yes",ages), rep("no",ages)), .before=everything())) %>% - bind_rows() %>% - setNames(c("year","kin","age_kin","alive",as.character(age))) %>% - gather(age_focal, count,-age_kin, -kin, -year, -alive) %>% - mutate(age_focal = as.integer(age_focal), + dplyr::bind_rows() %>% + stats::setNames(c("year","kin","age_kin","alive",as.character(age))) %>% + tidyr::gather(age_focal, count,-age_kin, -kin, -year, -alive) %>% + dplyr::mutate(age_focal = as.integer(age_focal), year = as.integer(year), cohort = year - age_focal) %>% - filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)])}) %>% - bind_rows() - return(kin) + dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)])}) %>% + dplyr::bind_rows() + + # results as list? + if(list_output) { + out <- kin_list + }else{ + out <- kin + } + + return(out) } @@ -141,13 +142,13 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, #' @description one time projection kin. internal function. #' -#' @param Ut numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year. -#' @param ft numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year. -#' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year. +#' @param Ut numeric. A matrix of survival probabilities (or ratios). +#' @param ft numeric. A matrix of age-specific fertility rates. +#' @param pit numeric. A matrix with distribution of childbearing. #' @param ages numeric. #' @param pkin numeric. A list with kin count distribution in previous year. # -timevarying_kin<- function(Ut,ft,pit,ages, pkin){ +timevarying_kin<- function(Ut, ft, pit, ages, pkin){ # frequently used zero vector for initial condition zvec=rep(0,ages*2); @@ -192,3 +193,30 @@ timevarying_kin<- function(Ut,ft,pit,ages, pkin){ return(kin_list) } + +#' defince apc combination to return + +#' @description defince apc to return. +#' +output_period_cohort_combination <- function(output_cohort = NULL, output_period = NULL, age = NULL, years_data = NULL){ + + # no specific + if(is.null(output_period) & is.null(output_cohort)){ + message("No specific output was set. Return all period data.") + output_period <- years_data + } + + # cohort combination + if(!is.null(output_cohort)){ + selected_cohorts_year_age <- data.frame(age = rep(age,length(output_cohort)), + year = purrr::map(output_cohort,.f = ~.x+age) %>% + unlist(use.names = F)) + }else{selected_cohorts_year_age <- c()} + + # period year combination + if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% dplyr::rename(age=1,year=2) + }else{selected_years_age <- c()} + + # end + return(dplyr::bind_rows(selected_years_age,selected_cohorts_year_age) %>% dplyr::distinct()) +} diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R index dbc3216..35d752d 100644 --- a/R/plot_diagramm.R +++ b/R/plot_diagramm.R @@ -13,7 +13,7 @@ plot_diagram <- function(kin_total, rounding = 3){ kin_total <- kin_total %>% mutate(count = round(count,digits = rounding)) - mermaid( + DiagrammeR::mermaid( paste0("graph TD GGM(ggm:
", kin_total$count[kin_total$kin=="ggm"] ,") diff --git a/README.Rmd b/README.Rmd index 353c905..0486f2e 100644 --- a/README.Rmd +++ b/README.Rmd @@ -83,7 +83,7 @@ Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the fr ## Next steps: -1. Implement multi-stage and two-sex matrix kinship models +1. Implement two-sex matrix kinship models 1. Create a hex logo for package 1. Improve kinship diagram visualization 1. Improve documentation and vignette of package diff --git a/README.md b/README.md index 4b60992..7feaadb 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ Population Biology 5(1):1–27. . ## Next steps: -1. Implement multi-stage and two-sex matrix kinship models +1. Implement two-sex matrix kinship models 2. Create a hex logo for package 3. Improve kinship diagram visualization 4. 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z)rYA=a)8BC@4eu7JIT$gwEi9#kEQ)E!ET-E>_6aVvrSZ(oz<3F6-BQg-X${Jv& zz*oD1cik?{sd#s%D0O(KP7I$n3Ky1{Vhx{tk+ERSygke9T)br!zgkWl!66>F5xAS zJuf5{b~4FX0o0Smc4#Y7>U2 z9sm%NaL+9#`1b+)@A2>*hNl`7`1(RB{B0g@L)Ip!OK<=)1z3H938`j~U}=#GD<)q0 z^!X3`!C!5h^Oed`K;3wPkI9kw4A@MXaZY_0SI#v7RE %\VignetteIndexEntry{Use} %\VignetteEngine{knitr::rmarkdown} @@ -15,132 +16,184 @@ library(tidyverse) library(knitr) ``` -The year is 2015 and focal is 35 years old^[Age should be understood as a class state, defined in a matrix transition framework.]. Focal and her relatives have all lived in Sweden. -We would like to know how many female relatives focal has in 2015, their mean age and the number of living kin at each age of focal. Finally, we would like to know at which age she had to suffer a death in average. +In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country. +Here, we'll show how `DemoKin` can be used to compute the number and age distribution of Focal's relatives under a range of assumptions, including living and deceased kin. -## Kin counts with time invariant rates +## 1. Kin counts with time-invariant rates -We can estimate this in a **stable** framework using data from 2015, assuming that all of focal's relatives experience mortality and fertility from that calendar year (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example (*swe_px*, *swe_Sx*, *swe_asfr* and *swe_pop*, matrix of age by year, with survival probability, survival ratio, fertility and population; type `data(package="DemoKin")`). This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/). These datasets were loaded using the`get_HMDHFD` function. +First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). + +In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of sruvival ratios and another vector of fertility rates. IN this example, we get the data for the year 2015, and run the matrix models: ```{r} -swe_surv_2015 <- swe_px[,"2015"] -swe_asfr_2015 <- swe_asfr[,"2015"] +library(tidyverse) +library(knitr) + +# First, get vectors for a given year +swe_surv_2015 <- DemoKin::swe_px[,"2015"] +swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] +# Run kinship models swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` -Let's visualize the distribution of relatives over focal's lifecourse: +### 1.1. Value + +`DemoKin::kin()` returns a list containing two data frames: `kin_full` and `kin_summary`. + +`kin_full` contains expected kin counts by year (or cohort), age of Focal and age of kin. Note that the columns `year` and `cohort` are empty if the argument if `time_invariant = TRUE` in `kin` (as in this example). + +```{r} +head(swe_2015$kin_full) +``` + +`kin_summary` is a ‘summary’ data frame derived from `kin_full`. + +```{r} +head(swe_2015$kin_summary) +``` + +To produce it, we sum over all ages of kin to produce a data frame of expected kin counts by year or cohort and age of Focal (but not by age of kin). +Consider this simplified example for living kin counts: + +```{r} +kin_summary_example <- + swe_2015$kin_full %>% + select(year, cohort, kin, age_focal, age_kin, living, death) %>% + group_by(year, cohort, kin, age_focal) %>% + summarise(count_living = sum(living)) + +head(kin_summary_example) +``` + +### 1.2. Visualizing the distribution of kin + +Let us now visualize the distribution of relatives over Focal's lifecourse using the summary data.frame `kin_summary`: ```{r, fig.height=6, fig.width=8} swe_2015[["kin_summary"]] %>% - ggplot() + - geom_line(aes(age_focal, count_living)) + - theme_bw() + - facet_wrap(~kin) + ggplot() + + geom_line(aes(age_focal, count_living)) + + theme_bw() + + labs(y = "Expected number of living relatives") + + facet_wrap(~kin) ``` -Where each relative type is identified by a unique code. Note that the relationship codes used in `DemoKin` differ from those in Caswell (2019). The equivalence between the two set of codes is given in the following table: +Here, each relative type is identified by a unique code. Note that `DemoKin` uses different codes than Caswell (2019); the equivalence between the two set of codes is given in the following table: ```{r, fig.height=6, fig.width=8, echo=FALSE} +library(knitr) -codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") -caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") -labels <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") - -data.frame(DemoKin = codes, Caswell = caswell_codes, Label = labels, row.names = NULL) %>% kable(caption = "Equivalence between relative codes between DemoKin and Caswell (2019).") - +DemoKin::demokin_codes() %>% + kable ``` -We can also visualize the age distribution of relatives when focal is 35 for example: +We can also visualize the age distribution of relatives when Focal is 35 years old (now, with full names to identify each relative type using the function `DemoKin::rename_kin()`:): ```{r, fig.height=6, fig.width=8} swe_2015[["kin_full"]] %>% - filter(age_focal == 35) %>% - ggplot() + - geom_line(aes(age_kin, living)) + - geom_vline(xintercept = 35, color=2) + - theme_bw() + - facet_wrap(~kin) + DemoKin::rename_kin() %>% + filter(age_focal == 35) %>% + ggplot() + + geom_line(aes(age_kin, living)) + + geom_vline(xintercept = 35, color=2) + + labs(y = "Expected number of living relatives") + + theme_bw() + + facet_wrap(~kin) ``` -The function assumes that fertility input is for both sex, so in case this is only for one you need to set `birth_female = 1` in a convenient way. In the case you are using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should consider the adaptation for person-year exposure on females: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$ (Preston et.al, 2002). +The one-sex model implemented in `DemoKin` assumes that the given fertility input applies to both sexes. + +Note that, if using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should account for female person-year exposure, using: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$ (see Preston et.al, 2002). -The `kin` function also includes a summary output with the count of living kin, mean and standard deviation of kin age, by type of kin, for each focal's age: +The `kin` function also includes a summary output with the count of living kin, mean and standard deviation of kin age, by type of kin, for each Focal's age: ```{r, fig.height=6, fig.width=8} swe_2015[["kin_summary"]] %>% + DemoKin::rename_kin() %>% filter(age_focal == 35) %>% select(kin, count_living, mean_age, sd_age) %>% mutate_if(is.numeric, round, 2) %>% kable() ``` -We can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35: +Finally, we can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35: -```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} -swe_2015_age35 <- swe_2015[["kin_summary"]] %>% - filter(age_focal == 35) %>% - select(kin, count = count_living) -plot_diagram(swe_2015_age35, rounding = 2) +```{r, fig.height=6, fig.width=8, dpi=900, message=FALSE, warning=FALSE} + swe_2015[["kin_summary"]] %>% + filter(age_focal == 35) %>% + select(kin, count = count_living) %>% + plot_diagram(rounding = 2) ``` -## Kin counts with time varying rates +## 2. Kin counts with time-variant rates -The demographic risks of population of Sweden is, in reality, changin every year, and focal relatives have been expossed to changing mortality and fertility rates. -To account for this, is needed estimations of kin counts in population with time-variant demographic rates. Let's take a look at the resulting kin counts for 1960 cohort, for some specific types: +The demography of Sweden is, in reality, changing every year. This means that Focal and her relatives will have experienced changing mortality and fertility rates over time. +We account for this, by using the time-variant models introduced by Caswell and Song (2021). +Let's take a look at the resulting kin counts for a Focal born in 1960, limiting the output to the relative types given in the argument `output_kin`: ```{r, fig.height=6, fig.width=8} -swe_2015_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant =FALSE, - output_cohort = 1960, - output_kin = c("d","gd","ggd","m","gm","ggm")) - -swe_2015_time_varying$kin_full %>% - group_by(kin, cohort, age_focal) %>% summarise(count = sum(living)) %>% - ggplot(aes(age_focal,count,color=factor(cohort))) + +swe_time_varying <- + kin( + U = swe_px, + f = swe_asfr, + N = swe_pop, + time_invariant =FALSE, + output_cohort = 1960, + output_kin = c("d","gd","ggd","m","gm","ggm") + ) + +swe_time_varying$kin_summary %>% + DemoKin::rename_kin() %>% + ggplot(aes(age_focal,count_living,color=factor(cohort))) + scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ - geom_line(size=1,color=1)+ + geom_line(color = 1)+ geom_vline(xintercept = 35, color=2)+ + labs(y = "Expected number of living relatives") + facet_wrap(~kin,scales = "free")+ theme_bw() ``` -As you see the estimates stop at age 55, because no data is after 2015. + -## Death Experience +## 3. Kin deaths -Kin loss has studied consequences related to mental health, care support and household incomes over life course. Setting the parameter `alive="no"` we can see for a focal from 1900 cohort how many relatives loss during her life (output `kin_death_by_age_focal`): +Kin loss can have severe consequences for bereaved relatives. It can also affect the provision of care support and intergenerational transfers over the life course. +The function `kin` also includes information on the number of relatives lost by Focal during her life, stored in the column `count_cum_death`: ```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} -swe_1900_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant = FALSE, - output_cohort = 1900, - output_kin = c("d","gd","ggd","m","gm","ggm")) - -swe_1900_time_varying$kin_summary %>% +swe_time_varying$kin_summary %>% + DemoKin::rename_kin() %>% ggplot() + - geom_line(aes(age_focal, count_cum_death)) + + geom_line(aes(age_focal, count_cum_death)) + + labs(y = "Expected number of deceased relatives") + theme_bw() + facet_wrap(~kin,scales="free") ``` -Given some portion of each relative was dead (remember we are talking about population measures), we can ask what was the mean age at lost for each kin, when focal was 50: +Given these population-level measures, we can compute Focal's the mean age at the time of her relative's death. For a Focal aged 50 yo: ```{r} -swe_1900_time_varying$kin_summary %>% +swe_time_varying$kin_summary %>% + DemoKin::rename_kin() %>% filter(age_focal == 50) %>% select(kin,count_cum_death,mean_age_lost) %>% mutate_if(is.numeric, round, 2) %>% kable() ``` -## Prevalences +## 4. Prevalences -Once you have a full kin distribution by age, is possible to know what is the expected portion of living kin in some stage given a set of prevalence by age (could be a disease, employment, etc.). This kind of method is known as Sullivan in life table apporaches. A matrix formulation for same results can be found in Caswell (2019). This can be applied for time variant prevalence too. +Given the distribution of kin by age, we can compute the expected portion of living kin in some stage given a set of prevalences by age (e.g., a disease, employment, etc.). This is known as the Sullivan Method in the life-table literature. A matrix formulation for same results can be found in Caswell (2019), which can also be extended to a time-variant framework. -```{r, message=FALSE, warning=FALSE} +```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10} # let´s create some prevalence by age -swe_2015_prevalence <- tibble(age_kin = unique(swe_2015$kin_full$age_kin), - prev = .005 * exp(.05 * age_kin)) +swe_2015_prevalence <- + tibble( + age_kin = unique(swe_2015$kin_full$age_kin), + prev = .005 * exp(.05 * age_kin) + ) # plot(swe_2015_prevalence) @@ -148,8 +201,11 @@ swe_2015_prevalence <- tibble(age_kin = unique(swe_2015$kin_full$age_kin), swe_2015$kin_full %>% left_join(swe_2015_prevalence) %>% group_by(kin, age_focal) %>% - summarise(prevalent = sum(living * prev), - no_prevalent = sum(living * (1-prev))) %>% + DemoKin::rename_kin() %>% + summarise( + prevalent = sum(living * prev), + no_prevalent = sum(living * (1-prev)) + ) %>% pivot_longer(cols = prevalent:no_prevalent, names_to = "prevalence_state", values_to = "count") %>% ggplot(aes(x=age_focal, y = count)) + geom_area(aes(fill=prevalence_state)) + @@ -158,25 +214,35 @@ swe_2015$kin_full %>% ``` -## Multi-state +## 5. Multi-state models -In `DemoKin` is possible to obtain kin structures in a multistate framework, classifying individuals jointly by age and other feature (like stages of some disease). For that is neccesary to use mortality and fertility for each possible stage (conditionated to), indicate in which age and state the new individuals (offspring) woul be destinated to,and the probabilities for changing state by age. +`DemoKin` allows the computation of kin structures in a multi-state framework, classifying individuals jointly by age and some other feature (e.g., stages of a disease). For this, we need mortality and fertility data for each possible stage and probabilities of changing state by age. -Let´s take the Caswell (2021) example for Slovakia where stages are parity states. The package presents data for the year 1980, where `svk_fxs` is the fertility rate by age (rows) for each parity stage (columns). The stage 1 is related to no kids (parity zero), the 2 is 1, and so on, until finally the 6 is 5 or more. The data.frame `svk_Hxs` has same structure but with $1$ in the age where newborns are destinated, in this case always to first age. The data.frame `svk_pxs` has same structure but with survivial probabilities. Finally we need a list `svk_Uxs` that has same number of elements than ages (in this case 110, where $omega$ is 109), and for each age there is a column-stochastic transition matrix with dimension for the state space, and the entries are transition probabilities conditional on survival. Based on Caswell (2020) matrix methods is possible to know joint age-parity kin structure. +Let's consider the example of Slovakia given by Caswell (2021), where stages are parity states. +`DemoKin` includes the data to replicate this analysis for the year 1980: -```{r} -# use birth_female=1 because fertility is for female only -demokin_svk1980_caswell2020 <- kin_multi_stage(U = svk_Uxs, - f = svk_fxs, - D = svk_pxs, - H = svk_Hxs, birth_female=1) +- The data.frame `svk_fxs` is the fertility rate by age (rows) for each parity stage (columns). The first stage represents $parity=0$; the second stage, $parity=1$; and so on, until finally the sixth stage represents $parity\geq5$. +- The data.frame `svk_Hxs` has a similar structure but with $1$'s in the ages corresponding to newborns (the first age in our example). +- The data.frame `svk_pxs` has the same structure and represents survival probabilities. +- The list `svk_Uxs` has the same number of elements and ages (in this case 110, where $omega$ is 109). For each age, it contains a column-stochastic transition matrix with dimension for the state space. The entries are transition probabilities conditional on survival. +Following Caswell (2020), we can obtain the joint age-parity kin structure: +```{r} +# use birth_female=1 because fertility is for female only +demokin_svk1980_caswell2020 <- + kin_multi_stage( + U = svk_Uxs, + f = svk_fxs, + D = svk_pxs, + H = svk_Hxs, + birth_female=1 + ) ``` -We can see age-parity distribution of aunts, when focal is 20 or 60 (similar to figure 4 in Caswell(2021)). +As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [2021]). -```{r, message=FALSE, warning=FALSE} +```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10} demokin_svk1980_caswell2020 %>% filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% mutate(parity = as.integer(stage_kin)-1, @@ -187,13 +253,14 @@ demokin_svk1980_caswell2020 %>% ggplot() + geom_bar(aes(x=age_kin, y = count, fill=parity), stat = "identity") + geom_vline(aes(xintercept = age_focal), col=2) + + labs(y = "Number of aunts") + theme_bw() + facet_wrap(~age_focal, nrow = 2) ``` -Also we can see, When focal grows up, the portion of living daughters and mothers in different parity stages (similar to figure 9 and 10 in Caswell(2021)). +We can also see the portion of living daughters and mothers at different parity stages over Focal's lie-course (this is equivalent to Figure 9 and 10 in Caswell [2021]). -```{r, message=FALSE, warning=FALSE} +```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10} demokin_svk1980_caswell2020 %>% filter(kin %in% c("d","m")) %>% mutate(parity = as.integer(stage_kin)-1, @@ -201,8 +268,10 @@ demokin_svk1980_caswell2020 %>% parity = fct_rev(parity)) %>% group_by(age_focal, kin, parity) %>% summarise(count= sum(living)) %>% + DemoKin::rename_kin() %>% ggplot() + geom_bar(aes(x=age_focal, y = count, fill=parity), stat = "identity") + + labs(y = "Kin count") + theme_bw() + facet_wrap(~kin, nrow = 2) ``` From 98fe8db29c76157fafd6d561eb99717798461c82 Mon Sep 17 00:00:00 2001 From: alburezg Date: Thu, 1 Sep 2022 10:41:25 +0200 Subject: [PATCH 11/21] fixed rename_kin --- R/aux_funs.R | 20 +++++++++++++------- vignettes/Reference.Rmd | 2 +- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/R/aux_funs.R b/R/aux_funs.R index 137ff67..e8c745b 100644 --- a/R/aux_funs.R +++ b/R/aux_funs.R @@ -12,31 +12,37 @@ demokin_codes <- function(){ #' rename kin #' @description Rename kin labels depending consolidate some types #' @export -rename_kin <- function(df, consolidate = FALSE){ +rename_kin <- function(df, consolidate_column = "no"){ - if(!consolidate){ + stopifnot("Argument 'consolidate_column' should be 'no' or a valid column name" = consolidate_column %in% c("no", colnames(df))) + + if(consolidate_column == "no"){ relatives <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") names(relatives) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") - } else if(consolidate){ + } else { # Combine kin types irrespective of whether they come from older # or younger sibling lines - consolidate <- c("c", "c", "d", "gd", "ggd", "ggm", "gm", "m", "n", "n", "a", "a", "s", "s") - names(consolidate) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") + consolidate_vec <- c("c", "c", "d", "gd", "ggd", "ggm", "gm", "m", "n", "n", "a", "a", "s", "s") + names(consolidate_vec) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") # Rename kin types from codes to actual words relatives <- c("Cousins", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces", "Aunt", "Sister") - names(relatives) <- unique(consolidate) + names(relatives) <- unique(consolidate_vec) + + df <- as.data.frame(df) + df$count <- df[ , consolidate_column] df <- df %>% - dplyr::mutate(kin = consolidate[kin]) %>% + dplyr::mutate(kin = consolidate_vec[kin]) %>% dplyr::group_by(age_focal, kin) %>% dplyr::summarise(count = sum(count)) %>% dplyr::ungroup() + } df$kin <- relatives[df$kin] df diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd index f1aaec4..2d923e6 100644 --- a/vignettes/Reference.Rmd +++ b/vignettes/Reference.Rmd @@ -87,7 +87,7 @@ DemoKin::demokin_codes() %>% kable ``` -We can also visualize the age distribution of relatives when Focal is 35 years old (now, with full names to identify each relative type using the function `DemoKin::rename_kin()`:): +We can also visualize the age distribution of relatives when Focal is 35 years old (now, with full names to identify each relative type using the function `DemoKin::rename_kin()`): ```{r, fig.height=6, fig.width=8} swe_2015[["kin_full"]] %>% From e911cf97fb2555a40950e68a9a5b14b14d422baf Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Fri, 2 Sep 2022 09:59:10 -0300 Subject: [PATCH 12/21] update docum --- DESCRIPTION | 2 +- NAMESPACE | 1 - R/get_HMDHFD.R | 70 ++++++++++++++++++++--------------------- R/kin.R | 12 +++---- R/kin_multi_stage.R | 7 +++-- R/kin_time_invariant.R | 2 +- R/kin_time_variant.R | 2 +- R/plot_diagramm.R | 7 ++--- README.Rmd | 7 ++--- README.md | 24 +++++++------- man/get_HMDHFD.Rd | 14 ++++----- man/kin_multi_stage.Rd | 4 ++- man/plot_diagram.Rd | 6 ++-- man/rename_kin.Rd | 2 +- vignettes/Reference.Rmd | 20 ++++++------ 15 files changed, 88 insertions(+), 92 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9d9e502..bcccca7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,7 @@ Suggests: rmarkdown, testthat (>= 3.0.0) VignetteBuilder: knitr -Imports: dplyr, tidyr, purrr, HMDHFDplus, devtools, progress, matrixcalc, Matrix, MASS, ggplot2, stats, igraph, DiagrammeR +Imports: dplyr, tidyr, purrr, HMDHFDplus, progress, matrixcalc, Matrix, MASS, stats, igraph BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index 513c4db..aef31f3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,4 +8,3 @@ export(kin_time_invariant) export(kin_time_variant) export(plot_diagram) export(rename_kin) -importFrom(DiagrammeR,mermaid) diff --git a/R/get_HMDHFD.R b/R/get_HMDHFD.R index d518eec..b4ee78e 100644 --- a/R/get_HMDHFD.R +++ b/R/get_HMDHFD.R @@ -1,16 +1,16 @@ #' Get time serie matrix data from HMD/HFD -#' @description Wrapper function to get data.frames of female survival, fertlity and population -#' of selected countriy on selected period. +#' @description Wrapper function to get data of female survival, fertlity and population +#' of selected country on selected period. #' @param country numeric. Country code from rom HMD/HFD. #' @param max_year numeric. Latest year to get data. #' @param min_year integer. Older year to get data. -#' @param user character. From HMD/HFD. -#' @param user_HMD character. From HMD/HFD. -#' @param user_HFD character. From HMD/HFD. -#' @param pass_HMD character. From HMD/HFD. -#' @param pass_HFD character. From HMD/HFD. +#' @param user_HMD character. From HMD. +#' @param user_HFD character. From HFD. +#' @param pass_HMD character. From HMD. +#' @param pass_HFD character. From HFD. +#' @param OAG numeric. Open age group to standarize output. #' @return A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames. #' @export @@ -28,13 +28,13 @@ get_HMDHFD <- function(country = "SWE", } # source HMD HFD ----------------------------------------------------------------- - pop <- readHMDweb(CNTRY = country, "Population", user_HMD, pass_HMD, fixup = TRUE) %>% - select(Year, Age, N = Female1)%>% - filter(Year >= min_year, Year <= max_year) - lt <- readHMDweb(country, "fltper_1x1", user_HMD, pass_HMD, fixup = TRUE) %>% - filter(Year >= min_year, Year <= max_year) - asfr <- readHFDweb(country, "asfrRR", user_HFD, pass_HFD, fixup = TRUE)%>% - filter(Year >= min_year, Year <= max_year) + pop <- HMDHFDplus::readHMDweb(CNTRY = country, "Population", user_HMD, pass_HMD, fixup = TRUE) %>% + dplyr::select(Year, Age, N = Female1)%>% + dplyr::filter(Year >= min_year, Year <= max_year) + lt <- HMDHFDplus::readHMDweb(country, "fltper_1x1", user_HMD, pass_HMD, fixup = TRUE) %>% + dplyr::filter(Year >= min_year, Year <= max_year) + asfr <- HMDHFDplus::readHFDweb(country, "asfrRR", user_HFD, pass_HFD, fixup = TRUE)%>% + dplyr::filter(Year >= min_year, Year <= max_year) # list of yearly Leslie matrix --------------------------------------------------- @@ -46,22 +46,22 @@ get_HMDHFD <- function(country = "SWE", # survival probability px <- lt %>% - filter(Age<=OAG) %>% - mutate(px = 1 - qx, + dplyr::filter(Age<=OAG) %>% + dplyr::mutate(px = 1 - qx, px = ifelse(Age==OAG, 0, px)) %>% - select(Year, Age, px) %>% - pivot_wider(names_from = "Year", values_from = "px") %>% - select(-Age) %>% + dplyr::select(Year, Age, px) %>% + tidyr::pivot_wider(names_from = "Year", values_from = "px") %>% + dplyr::select(-Age) %>% as.matrix() rownames(px) = 0:OAG # survival function Lx <- lt %>% - filter(Age<=OAG) %>% - mutate(Lx = ifelse(Age==OAG, Tx, Lx)) %>% - select(Year, Age, Lx) %>% - pivot_wider(names_from = "Year", values_from = "Lx") %>% - select(-Age) %>% + dplyr::filter(Age<=OAG) %>% + dplyr::mutate(Lx = ifelse(Age==OAG, Tx, Lx)) %>% + dplyr::select(Year, Age, Lx) %>% + tidyr::pivot_wider(names_from = "Year", values_from = "Lx") %>% + dplyr::select(-Age) %>% as.matrix() Sx <- rbind(Lx[c(-1,-ages),]/Lx[-c(w:ages),], @@ -71,26 +71,26 @@ get_HMDHFD <- function(country = "SWE", # fertility fx <- asfr %>% - filter(Year >= min_year) %>% - select(-OpenInterval) %>% + dplyr::filter(Year >= min_year) %>% + dplyr::select(-OpenInterval) %>% rbind( expand.grid(Year = years, Age = c(0:(min(asfr$Age)-1),(max(asfr$Age)+1):OAG), ASFR = 0)) %>% - arrange(Year, Age) %>% - spread(Year, ASFR) %>% - select(-Age) %>% + dplyr::arrange(Year, Age) %>% + tidyr::spread(Year, ASFR) %>% + dplyr::select(-Age) %>% as.matrix() rownames(fx) = 0:OAG # population Nx <- pop %>% - mutate(Age = ifelse(Age>OAG, OAG, Age)) %>% - group_by(Year, Age) %>% summarise(N = sum(N)) %>% - filter(Age<=OAG, Year >= min_year) %>% - arrange(Year, Age) %>% - spread(Year, N) %>% - select(-Age) %>% + dplyr::mutate(Age = ifelse(Age>OAG, OAG, Age)) %>% + dplyr::group_by(Year, Age) %>% summarise(N = sum(N)) %>% + dplyr::filter(Age<=OAG, Year >= min_year) %>% + dplyr::arrange(Year, Age) %>% + tidyr::spread(Year, N) %>% + dplyr::select(-Age) %>% as.matrix() rownames(Nx) = 0:OAG diff --git a/R/kin.R b/R/kin.R index 3ceed5a..3efab30 100644 --- a/R/kin.R +++ b/R/kin.R @@ -61,7 +61,7 @@ kin <- function(U = NULL, f = NULL, } # reorder - kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, living, death) + kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, living, dead) # summary # select period/cohort @@ -84,15 +84,15 @@ kin <- function(U = NULL, f = NULL, sd_age = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>% tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"), kin_full %>% - dplyr::rename(total=death) %>% + dplyr::rename(total=dead) %>% dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>% - dplyr::summarise(count_death = sum(total)) %>% + dplyr::summarise(count_dead = sum(total)) %>% dplyr::ungroup() %>% dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>% - dplyr::mutate(count_cum_death = cumsum(count_death), - mean_age_lost = cumsum(count_death * age_focal)/cumsum(count_death)) %>% + dplyr::mutate(count_cum_dead = cumsum(count_dead), + mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>% dplyr::ungroup() %>% - tidyr::pivot_longer(count_death:mean_age_lost, names_to = "indicator", "value")) %>% + tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", "value")) %>% dplyr::ungroup() %>% tidyr::pivot_wider(names_from = indicator, values_from = value) diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R index ccb0a89..7129134 100644 --- a/R/kin_multi_stage.R +++ b/R/kin_multi_stage.R @@ -7,6 +7,7 @@ #' @param D matrix. survival probabilities by state (age in rows and states in columns) #' @param H matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns). #' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. +#' @param birth_female numeric. Female portion at birth. #' @param list_output logical. Results as a list. Default `FALSE`. #' @return A data frame with focal´s age, related ages and type of kin @@ -15,7 +16,7 @@ #' kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, - birth_female = 1, + birth_female = 1/2.04, output_kin = NULL, list_output = FALSE){ @@ -149,7 +150,7 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, # only selected kin if(!is.null(output_kin)){ - kin_list <- kin_list %>% keep(names(.) %in% output_kin) + kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin) } # as data.frame @@ -161,7 +162,7 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, dplyr::mutate(kin = y, age_kin = rep(sort(rep(age,s)),2), stage_kin = rep(rep(1:s,ages),2), - alive = c(rep("living",s*ages),rep("death",s*ages)) + alive = c(rep("living",s*ages),rep("dead",s*ages)) # age_kin = sort(rep(age,s)), # stage_kin = rep(1:s,ages), # alive = c(rep("yes",s*ages)) diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R index 23df06d..d843c4e 100644 --- a/R/kin_time_invariant.R +++ b/R/kin_time_invariant.R @@ -103,7 +103,7 @@ kin_time_invariant <- function(U = NULL, f = NULL, out %>% dplyr::mutate(kin = y, age_kin = rep(age,2), - alive = c(rep("living",ages), rep("death",ages))) %>% + alive = c(rep("living",ages), rep("dead",ages))) %>% tidyr::pivot_longer(c(-age_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% dplyr::mutate(age_focal = as.integer(age_focal)) %>% tidyr::pivot_wider(names_from = alive, values_from = count) diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index ff69a19..adb8c8e 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -116,7 +116,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, dplyr::mutate(year = Y, kin=y, age_kin = rep(age,2), - alive = c(rep("living",ages), rep("death",ages)), + alive = c(rep("living",ages), rep("dead",ages)), .before=everything())) %>% dplyr::bind_rows() %>% stats::setNames(c("year","kin","age_kin","alive",as.character(age))) %>% diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R index 1604108..2497186 100644 --- a/R/plot_diagramm.R +++ b/R/plot_diagramm.R @@ -1,14 +1,13 @@ #' plot a Kin diagram (network) -#' @description Given estimation of kin counts from `kins` function, draw a diagramm. -#' @param kins_total data.frame. kin type and some count in columns. +#' @description Given estimation of kin counts from `kins` function, draw a network diagramm. +#' @param kin_total data.frame. With columns `kin` with type and `count` with some measeure. #' @param rounding numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm. #' @return A plot -#' @importFrom DiagrammeR mermaid #' @export plot_diagram <- function(kin_total, rounding = 3){ - library(igraph) + vertices <- data.frame( nodes = c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys") , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3) diff --git a/README.Rmd b/README.Rmd index 651c82b..89c2614 100644 --- a/README.Rmd +++ b/README.Rmd @@ -48,8 +48,8 @@ Let's explore this Using the Swedish data included with `DemoKin`. ```{r, fig.height=6, fig.width=8} library(DemoKin) -swe_surv_2015 <- DemoKin::swe_px[,"2015"] -swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] +swe_surv_2015 <- swe_px[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` @@ -59,7 +59,7 @@ Now, we can visualize the implied kin counts (i.e., the average number of living ```{r, fig.height=6, dpi=1200,fig.width=8, message=FALSE, warning=FALSE} # We need to reformat the data a little bit -kin_total <- swe_2015[["kin_summary"]] +kin_total <- swe_2015$kin_summary # Keep only data for Focal's age 35 kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")] names(kin_total) <- c("kin", "count") @@ -69,7 +69,6 @@ plot_diagram(kin_total, rounding = 2) Relatives are identified by a unique code: ```{r, fig.height=6, fig.width=8, echo=FALSE} - kable(DemoKin::demokin_codes()[-2]) ``` diff --git a/README.md b/README.md index c72bc32..6c7f1a9 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ # DemoKin: Matrix kinship models in R -Aug 31 2022 +sep. 02 2022 `DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and @@ -39,8 +39,8 @@ Let’s explore this Using the Swedish data included with `DemoKin`. ``` r library(DemoKin) -swe_surv_2015 <- DemoKin::swe_px[,"2015"] -swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] +swe_surv_2015 <- swe_px[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` @@ -53,7 +53,7 @@ diagram with the function `plot_diagram`: ``` r # We need to reformat the data a little bit -kin_total <- swe_2015[["kin_summary"]] +kin_total <- swe_2015$kin_summary # Keep only data for Focal's age 35 kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")] names(kin_total) <- c("kin", "count") @@ -65,7 +65,7 @@ plot_diagram(kin_total, rounding = 2) Relatives are identified by a unique code: | DemoKin | Label | -| :------ | :------------------------- | +|:--------|:---------------------------| | coa | Cousins from older aunt | | cya | Cousins from younger aunt | | d | Daughter | @@ -84,10 +84,10 @@ Relatives are identified by a unique code: ## Vignette For more details, including an extension to time varying-populations -rates, deceased kin, and multi-state models, see `vignette("Reference", -package = "DemoKin")`. If the vignette does not load, you may need to -install the package as `devtools::install_github("IvanWilli/DemoKin", -build_vignettes = T)`. +rates, deceased kin, and multi-state models, see +`vignette("Reference", package = "DemoKin")`. If the vignette does not +load, you may need to install the package as +`devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation @@ -95,11 +95,11 @@ Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: . -## Get involved\! +## Get involved! `DemoKin` is under constant development. If you’re interested in contributing, please get in touch, create an issue, or submit a pull -request. We look forward to hearing from you\! +request. We look forward to hearing from you! ## References @@ -120,7 +120,5 @@ and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. . - - diff --git a/man/get_HMDHFD.Rd b/man/get_HMDHFD.Rd index 6c2ee13..9bbf264 100644 --- a/man/get_HMDHFD.Rd +++ b/man/get_HMDHFD.Rd @@ -22,20 +22,20 @@ get_HMDHFD( \item{max_year}{numeric. Latest year to get data.} -\item{user_HMD}{character. From HMD/HFD.} +\item{user_HMD}{character. From HMD.} -\item{pass_HMD}{character. From HMD/HFD.} +\item{pass_HMD}{character. From HMD.} -\item{user_HFD}{character. From HMD/HFD.} +\item{user_HFD}{character. From HFD.} -\item{pass_HFD}{character. From HMD/HFD.} +\item{pass_HFD}{character. From HFD.} -\item{user}{character. From HMD/HFD.} +\item{OAG}{numeric. Open age group to standarize output.} } \value{ A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames. } \description{ -Wrapper function to get data.frames of female survival, fertlity and population -of selected countriy on selected period. +Wrapper function to get data of female survival, fertlity and population +of selected country on selected period. } diff --git a/man/kin_multi_stage.Rd b/man/kin_multi_stage.Rd index 519a4a5..12fa213 100644 --- a/man/kin_multi_stage.Rd +++ b/man/kin_multi_stage.Rd @@ -9,7 +9,7 @@ kin_multi_stage( f = NULL, D = NULL, H = NULL, - birth_female = 1, + birth_female = 1/2.04, output_kin = NULL, list_output = FALSE ) @@ -23,6 +23,8 @@ kin_multi_stage( \item{H}{matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns).} +\item{birth_female}{numeric. Female portion at birth.} + \item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.} \item{list_output}{logical. Results as a list. Default \code{FALSE}.} diff --git a/man/plot_diagram.Rd b/man/plot_diagram.Rd index 4068c74..8d2448c 100644 --- a/man/plot_diagram.Rd +++ b/man/plot_diagram.Rd @@ -7,13 +7,13 @@ plot_diagram(kin_total, rounding = 3) } \arguments{ -\item{rounding}{numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm.} +\item{kin_total}{data.frame. With columns \code{kin} with type and \code{count} with some measeure.} -\item{kins_total}{data.frame. kin type and some count in columns.} +\item{rounding}{numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm.} } \value{ A plot } \description{ -Given estimation of kin counts from \code{kins} function, draw a diagramm. +Given estimation of kin counts from \code{kins} function, draw a network diagramm. } diff --git a/man/rename_kin.Rd b/man/rename_kin.Rd index 32b7ae4..023cf99 100644 --- a/man/rename_kin.Rd +++ b/man/rename_kin.Rd @@ -4,7 +4,7 @@ \alias{rename_kin} \title{rename kin} \usage{ -rename_kin(df, consolidate = FALSE) +rename_kin(df, consolidate_column = "no") } \description{ Rename kin labels depending consolidate some types diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd index 2d923e6..28f1a5b 100644 --- a/vignettes/Reference.Rmd +++ b/vignettes/Reference.Rmd @@ -12,8 +12,6 @@ vignette: > ```{r, include=FALSE} devtools::load_all() -library(tidyverse) -library(knitr) ``` In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country. @@ -25,13 +23,13 @@ First, we compute kin counts in a **time-invariant** framework. We assume that F In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of sruvival ratios and another vector of fertility rates. IN this example, we get the data for the year 2015, and run the matrix models: -```{r} +```{r, message=FALSE, warning=FALSE} +library(DemoKin) library(tidyverse) library(knitr) - # First, get vectors for a given year -swe_surv_2015 <- DemoKin::swe_px[,"2015"] -swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] +swe_surv_2015 <- swe_px[,"2015"] +swe_asfr_2015 <- swe_asfr[,"2015"] # Run kinship models swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` @@ -58,7 +56,7 @@ Consider this simplified example for living kin counts: ```{r} kin_summary_example <- swe_2015$kin_full %>% - select(year, cohort, kin, age_focal, age_kin, living, death) %>% + select(year, cohort, kin, age_focal, age_kin, living, dead) %>% group_by(year, cohort, kin, age_focal) %>% summarise(count_living = sum(living)) @@ -166,7 +164,7 @@ The function `kin` also includes information on the number of relatives lost by swe_time_varying$kin_summary %>% DemoKin::rename_kin() %>% ggplot() + - geom_line(aes(age_focal, count_cum_death)) + + geom_line(aes(age_focal, count_cum_dead)) + labs(y = "Expected number of deceased relatives") + theme_bw() + facet_wrap(~kin,scales="free") @@ -176,9 +174,9 @@ Given these population-level measures, we can compute Focal's the mean age at th ```{r} swe_time_varying$kin_summary %>% - DemoKin::rename_kin() %>% + rename_kin() %>% filter(age_focal == 50) %>% - select(kin,count_cum_death,mean_age_lost) %>% + select(kin,count_cum_dead,mean_age_lost) %>% mutate_if(is.numeric, round, 2) %>% kable() ``` @@ -201,7 +199,7 @@ swe_2015_prevalence <- swe_2015$kin_full %>% left_join(swe_2015_prevalence) %>% group_by(kin, age_focal) %>% - DemoKin::rename_kin() %>% + rename_kin() %>% summarise( prevalent = sum(living * prev), no_prevalent = sum(living * (1-prev)) From 225d8da71375111df2a8077b03e680912c77b992 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Fri, 2 Sep 2022 10:28:09 -0300 Subject: [PATCH 13/21] use pipe --- DESCRIPTION | 13 ++++++++++++- NAMESPACE | 2 ++ R/kin.R | 1 + R/utils-pipe.R | 14 ++++++++++++++ man/pipe.Rd | 20 ++++++++++++++++++++ 5 files changed, 49 insertions(+), 1 deletion(-) create mode 100644 R/utils-pipe.R create mode 100644 man/pipe.Rd diff --git a/DESCRIPTION b/DESCRIPTION index bcccca7..abfe2f5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,18 @@ Suggests: rmarkdown, testthat (>= 3.0.0) VignetteBuilder: knitr -Imports: dplyr, tidyr, purrr, HMDHFDplus, progress, matrixcalc, Matrix, MASS, stats, igraph +Imports: + dplyr, + tidyr, + purrr, + HMDHFDplus, + progress, + matrixcalc, + Matrix, + MASS, + stats, + igraph, + magrittr BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index aef31f3..f5c7de1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export("%>%") export(demokin_codes) export(get_HMDHFD) export(kin) @@ -8,3 +9,4 @@ export(kin_time_invariant) export(kin_time_variant) export(plot_diagram) export(rename_kin) +importFrom(magrittr,"%>%") diff --git a/R/kin.R b/R/kin.R index 3efab30..af9869e 100644 --- a/R/kin.R +++ b/R/kin.R @@ -32,6 +32,7 @@ kin <- function(U = NULL, f = NULL, age <- as.integer(rownames(U)) years_data <- as.integer(colnames(U)) + `%>%` <- magrittr::`%>%` # kin to return all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") diff --git a/R/utils-pipe.R b/R/utils-pipe.R new file mode 100644 index 0000000..fd0b1d1 --- /dev/null +++ b/R/utils-pipe.R @@ -0,0 +1,14 @@ +#' Pipe operator +#' +#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +#' +#' @name %>% +#' @rdname pipe +#' @keywords internal +#' @export +#' @importFrom magrittr %>% +#' @usage lhs \%>\% rhs +#' @param lhs A value or the magrittr placeholder. +#' @param rhs A function call using the magrittr semantics. +#' @return The result of calling `rhs(lhs)`. +NULL diff --git a/man/pipe.Rd b/man/pipe.Rd new file mode 100644 index 0000000..a648c29 --- /dev/null +++ b/man/pipe.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils-pipe.R +\name{\%>\%} +\alias{\%>\%} +\title{Pipe operator} +\usage{ +lhs \%>\% rhs +} +\arguments{ +\item{lhs}{A value or the magrittr placeholder.} + +\item{rhs}{A function call using the magrittr semantics.} +} +\value{ +The result of calling \code{rhs(lhs)}. +} +\description{ +See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +} +\keyword{internal} From 78629dc1ddd600972ab758d667003f8298d1d8a4 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Fri, 2 Sep 2022 16:57:25 -0300 Subject: [PATCH 14/21] =?UTF-8?q?Sha=C2=B4s=20correction?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- R/kin_time_variant.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index adb8c8e..bc962fc 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -84,7 +84,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, U1 <- c(diag(Ut[-1,])[1:om],Ut[om,om]) f1 <- ft[1,][1:ages] pi1 <- pit[1:ages] - kin_all[[1]] <- kin_time_invariant(U = U1, f = f1, pi = pi1, birth_female = birth_female, + kin_all[[1]] <- kin_time_invariant(U = U1, f = f1/birth_female, pi = pi1, birth_female = birth_female, list_output = TRUE) } kin_all[[iyear+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[iyear]]) @@ -166,7 +166,7 @@ timevarying_kin<- function(Ut, ft, pit, ages, pkin){ gm[1:ages,1] = pkin[["m"]][1:ages,] %*% pit[1:ages] ggm[1:ages,1]= pkin[["gm"]][1:ages,] %*% pit[1:ages] os[1:ages,1] = pkin[["d"]][1:ages,] %*% pit[1:ages] - ys[1:ages,1] = pkin[["gd"]][1:ages,] %*% pit[1:ages] + nos[1:ages,1] = pkin[["gd"]][1:ages,] %*% pit[1:ages] oa[1:ages,1] = pkin[["os"]][1:ages,] %*% pit[1:ages] ya[1:ages,1] = pkin[["ys"]][1:ages,] %*% pit[1:ages] coa[1:ages,1]= pkin[["nos"]][1:ages,] %*% pit[1:ages] From 42d94faebe166c8b8990a7304cb9797d62d9c717 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Thu, 8 Sep 2022 09:03:10 -0300 Subject: [PATCH 15/21] pre release details --- DESCRIPTION | 3 +- R/kin.R | 37 +++++++++++++++-------- README.Rmd | 24 +++++++++------ README.md | 27 +++++++++++------ man/figures/README-unnamed-chunk-4-1.png | Bin 0 -> 199981 bytes man/kin.Rd | 28 +++++++++-------- 6 files changed, 74 insertions(+), 45 deletions(-) create mode 100644 man/figures/README-unnamed-chunk-4-1.png diff --git a/DESCRIPTION b/DESCRIPTION index abfe2f5..f93069c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -27,7 +27,8 @@ Imports: MASS, stats, igraph, - magrittr + magrittr, + lifecycle BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) diff --git a/R/kin.R b/R/kin.R index af9869e..37ee0d6 100644 --- a/R/kin.R +++ b/R/kin.R @@ -1,8 +1,8 @@ #' Estimate kin counts #' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. -#' @details See Caswell (2019) and Caswell (2021) for details on formulas. -#' @param U numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). +#' @details See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by deafult). +#' @param U numeric. A vector (atomic) or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). #' @param f numeric. Same as U but for fertility rates. #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`. #' @param N numeric. Same as U but for population distribution (counts or `%`). Optional. @@ -10,16 +10,22 @@ #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range. #' @param output_period integer. Vector of period years for returning results. Should be within input data years range. #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,... -#' @param birth_female numeric. Female portion at birth. +#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1. #' @return A list with: -#' * `kin_full`: a data frame with year, cohort, focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age. -#' * `kin_summary`: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending arguments): -#' - `count_living`: count of living kin at actual age of focal -#' - `mean_age`: mean age of each type of living kin. -#' - `sd_age`: standard deviation age of each type of living kin . -#' - `count_death`: count of death kin at specific age of focal. -#' - `count_cum_death`: cumulated count of death kin until specific age of focal. -#' - `mean_age_lost`: mean age where focal lost her relative. +#' \itemize{ +#' \item{kin_full}{a data frame with year, cohort, focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age.} +#' \item{kin_summary}{a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending arguments):} +#' {\itemize{ +#' \item{`count_living`}{: count of living kin at actual age of focal} +#' \item{`mean_age`}{: mean age of each type of living kin.} +#' \item{`sd_age`}{: standard deviation age of each type of living kin .} +#' \item{`count_death`}{: count of death kin at specific age of focal.} +#' \item{`count_cum_death`}{: cumulated count of death kin until specific age of focal.} +#' \item{`mean_age_lost`}{: mean age where focal lost her relative.} +#' } +#' } +#' } + #' @export #' # get kin ---------------------------------------------------------------- @@ -27,12 +33,17 @@ kin <- function(U = NULL, f = NULL, time_invariant = TRUE, N = NULL, pi = NULL, output_cohort = NULL, output_period = NULL, output_kin=NULL, - birth_female = 1/2.04) + birth_female = 1/2.04, + stable = lifecycle::deprecated()) { age <- as.integer(rownames(U)) years_data <- as.integer(colnames(U)) - `%>%` <- magrittr::`%>%` + + if (lifecycle::is_present(stable)) { + lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant") + time_invariant <- stable + } # kin to return all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") diff --git a/README.Rmd b/README.Rmd index 89c2614..7b7bec4 100644 --- a/README.Rmd +++ b/README.Rmd @@ -17,15 +17,22 @@ library(knitr) ``` -```{r, out.width = "250px", fig.align = 'right', echo=F} -knitr::include_graphics("DemoKin-Logo.png") -``` +# DemoKin + +:::::::::::::: {.columns} +::: {.column width="60%"} -# DemoKin: Matrix kinship models in R -`r format(Sys.time(), "%b %d %Y")` `DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019), Caswell (2020), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974). +::: +::: {.column width="40%"} + + + +::: +:::::::::::::: + ## Installation You can install the development version from GitHub with: @@ -35,16 +42,15 @@ You can install the development version from GitHub with: devtools::install_github("IvanWilli/DemoKin") ``` -## Time-invariant example +## Usage Consider an average Swedish woman called 'Focal'. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life (the 'time-invariant' assumption in Caswell [2019]). We then ask: - > How many living relatives does Focal have at each age? -Let's explore this Using the Swedish data included with `DemoKin`. +Let's explore this using the Swedish data already included with `DemoKin`. ```{r, fig.height=6, fig.width=8} library(DemoKin) @@ -53,7 +59,7 @@ swe_asfr_2015 <- swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` -*px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). +*px* is the survival probability by age from a life table and *f* are the age specific fertility raties by age (see `?kin` for details). Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`: diff --git a/README.md b/README.md index 6c7f1a9..49ea873 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ - +# DemoKin -# DemoKin: Matrix kinship models in R +
    -sep. 02 2022 +
    `DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and @@ -13,6 +13,16 @@ assumptions. The package is an R-language implementation of Caswell (2019), Caswell (2020), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974). +
    + +
    + + + +
    + +
    + ## Installation You can install the development version from GitHub with: @@ -22,7 +32,7 @@ You can install the development version from GitHub with: devtools::install_github("IvanWilli/DemoKin") ``` -## Time-invariant example +## Usage Consider an average Swedish woman called ‘Focal’. For this exercise, we assume a female closed population in which everyone experiences the @@ -31,11 +41,10 @@ life (the ‘time-invariant’ assumption in Caswell \[2019\]). We then ask: - - > How many living relatives does Focal have at each age? -Let’s explore this Using the Swedish data included with `DemoKin`. +Let’s explore this using the Swedish data already included with +`DemoKin`. ``` r library(DemoKin) @@ -45,7 +54,7 @@ swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` *px* is the survival probability by age from a life table and *f* are -the age specific fertility ratios by age (see `?kin` for details). +the age specific fertility raties by age (see `?kin` for details). 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zj3T)15ovmFyk0(UlWX9IUO{2!hTSM=S-2?U|8-TK?i|L-4LOmok|95>b1 z&E6upS69s_gR=0SJWDyA-OLiqpy literal 0 HcmV?d00001 diff --git a/man/kin.Rd b/man/kin.Rd index 5dfbb9d..ba57ec8 100644 --- a/man/kin.Rd +++ b/man/kin.Rd @@ -13,11 +13,12 @@ kin( output_cohort = NULL, output_period = NULL, output_kin = NULL, - birth_female = 1/2.04 + birth_female = 1/2.04, + stable = lifecycle::deprecated() ) } \arguments{ -\item{U}{numeric. A vector or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).} +\item{U}{numeric. A vector (atomic) or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).} \item{f}{numeric. Same as U but for fertility rates.} @@ -33,26 +34,27 @@ kin( \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...} -\item{birth_female}{numeric. Female portion at birth.} +\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.} } \value{ A list with: \itemize{ -\item \code{kin_full}: a data frame with year, cohort, focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age. -\item \code{kin_summary}: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending arguments): +\item{kin_full}{a data frame with year, cohort, focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age.} +\item{kin_summary}{a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending arguments):} +{\itemize{ +\item{\code{count_living}}{: count of living kin at actual age of focal} +\item{\code{mean_age}}{: mean age of each type of living kin.} +\item{\code{sd_age}}{: standard deviation age of each type of living kin .} +\item{\code{count_death}}{: count of death kin at specific age of focal.} +\item{\code{count_cum_death}}{: cumulated count of death kin until specific age of focal.} +\item{\code{mean_age_lost}}{: mean age where focal lost her relative.} +} } -\itemize{ -\item \code{count_living}: count of living kin at actual age of focal -\item \code{mean_age}: mean age of each type of living kin. -\item \code{sd_age}: standard deviation age of each type of living kin . -\item \code{count_death}: count of death kin at specific age of focal. -\item \code{count_cum_death}: cumulated count of death kin until specific age of focal. -\item \code{mean_age_lost}: mean age where focal lost her relative. } } \description{ Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. } \details{ -See Caswell (2019) and Caswell (2021) for details on formulas. +See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by deafult). } From 0119d21f703076356f9788a53101867a539ab640 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Thu, 8 Sep 2022 16:39:16 -0300 Subject: [PATCH 16/21] pre realease minor ch --- R/kin.R | 16 ++++++++-------- README.Rmd | 4 ++++ README.md | 10 ++++++++++ man/kin.Rd | 16 ++++++++-------- vignettes/Reference.Rmd | 4 ++-- 5 files changed, 32 insertions(+), 18 deletions(-) diff --git a/R/kin.R b/R/kin.R index 37ee0d6..e9a5669 100644 --- a/R/kin.R +++ b/R/kin.R @@ -1,7 +1,7 @@ #' Estimate kin counts #' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. -#' @details See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by deafult). +#' @details See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default). #' @param U numeric. A vector (atomic) or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year). #' @param f numeric. Same as U but for fertility rates. #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`. @@ -13,15 +13,15 @@ #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1. #' @return A list with: #' \itemize{ -#' \item{kin_full}{a data frame with year, cohort, focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age.} -#' \item{kin_summary}{a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending arguments):} +#' \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.} +#' \item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):} #' {\itemize{ -#' \item{`count_living`}{: count of living kin at actual age of focal} +#' \item{`count_living`}{: count of living kin at actual age of Focal} #' \item{`mean_age`}{: mean age of each type of living kin.} -#' \item{`sd_age`}{: standard deviation age of each type of living kin .} -#' \item{`count_death`}{: count of death kin at specific age of focal.} -#' \item{`count_cum_death`}{: cumulated count of death kin until specific age of focal.} -#' \item{`mean_age_lost`}{: mean age where focal lost her relative.} +#' \item{`sd_age`}{: standard deviation of age of each type of living kin.} +#' \item{`count_death`}{: count of dead kin at specific age of Focal.} +#' \item{`count_cum_death`}{: cumulated count of dead kin until specific age of Focal.} +#' \item{`mean_age_lost`}{: mean age where Focal lost her relative.} #' } #' } #' } diff --git a/README.Rmd b/README.Rmd index 7b7bec4..5eb0592 100644 --- a/README.Rmd +++ b/README.Rmd @@ -87,6 +87,10 @@ If the vignette does not load, you may need to install the package as `devtools: Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: https://github.com/IvanWilli/DemoKin. +## Acknowledgments + + We thank Silvia Leek from the Max Planck Institute for Demographic Research for designing the DemoKin logo. The logo includes elements that have been taken or adapted [from this file](https://commons.wikimedia.org/wiki/File:Escudo_de_la_Orden_de_San_Jer%C3%B3nimo.svg), originally by Ansunando, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0) via Wikimedia Commons. Sha Jiang provided useful comments for improving the package. + ## Get involved! `DemoKin` is under constant development. diff --git a/README.md b/README.md index 49ea873..0142d8b 100644 --- a/README.md +++ b/README.md @@ -104,6 +104,16 @@ Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: . +## Acknowledgments + +We thank Silvia Leek from the Max Planck Institute for Demographic +Research for designing the DemoKin logo. The logo includes elements that +have been taken or adapted [from this +file](https://commons.wikimedia.org/wiki/File:Escudo_de_la_Orden_de_San_Jer%C3%B3nimo.svg), +originally by Ansunando, [CC BY-SA +4.0](https://creativecommons.org/licenses/by-sa/4.0) via Wikimedia +Commons. Sha Jiang provided useful comments for improving the package. + ## Get involved! `DemoKin` is under constant development. If you’re interested in diff --git a/man/kin.Rd b/man/kin.Rd index ba57ec8..21c8115 100644 --- a/man/kin.Rd +++ b/man/kin.Rd @@ -39,15 +39,15 @@ kin( \value{ A list with: \itemize{ -\item{kin_full}{a data frame with year, cohort, focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age.} -\item{kin_summary}{a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending arguments):} +\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age.} +\item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending on the given arguments):} {\itemize{ -\item{\code{count_living}}{: count of living kin at actual age of focal} +\item{\code{count_living}}{: count of living kin at actual age of Focal} \item{\code{mean_age}}{: mean age of each type of living kin.} -\item{\code{sd_age}}{: standard deviation age of each type of living kin .} -\item{\code{count_death}}{: count of death kin at specific age of focal.} -\item{\code{count_cum_death}}{: cumulated count of death kin until specific age of focal.} -\item{\code{mean_age_lost}}{: mean age where focal lost her relative.} +\item{\code{sd_age}}{: standard deviation of age of each type of living kin.} +\item{\code{count_death}}{: count of dead kin at specific age of Focal.} +\item{\code{count_cum_death}}{: cumulated count of dead kin until specific age of Focal.} +\item{\code{mean_age_lost}}{: mean age where Focal lost her relative.} } } } @@ -56,5 +56,5 @@ A list with: Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. } \details{ -See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by deafult). +See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default). } diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd index 28f1a5b..388a072 100644 --- a/vignettes/Reference.Rmd +++ b/vignettes/Reference.Rmd @@ -21,7 +21,7 @@ Here, we'll show how `DemoKin` can be used to compute the number and age distrib First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). -In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of sruvival ratios and another vector of fertility rates. IN this example, we get the data for the year 2015, and run the matrix models: +In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of sruvival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models: ```{r, message=FALSE, warning=FALSE} library(DemoKin) @@ -38,7 +38,7 @@ swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) `DemoKin::kin()` returns a list containing two data frames: `kin_full` and `kin_summary`. -`kin_full` contains expected kin counts by year (or cohort), age of Focal and age of kin. Note that the columns `year` and `cohort` are empty if the argument if `time_invariant = TRUE` in `kin` (as in this example). +`kin_full` contains expected kin counts by year (or cohort), age of Focal and age of kin. Note that the columns `year` and `cohort` are empty if the argument is `time_invariant = TRUE` in `kin` (as in this example). ```{r} head(swe_2015$kin_full) From d59e06207d68921c5a38feb736fefd95ea637449 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Thu, 8 Sep 2022 16:44:53 -0300 Subject: [PATCH 17/21] Increment version number to 1.0.0 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index f93069c..f14db16 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: DemoKin Title: Demokin Description: Estimate population kin counts and its distribution by type and age -Version: 0.0.0.9000 +Version: 1.0.0 Authors@R: c( person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"), person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"), From 9499ea759bd280636f4084ac778f9f662e670b90 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Thu, 8 Sep 2022 16:49:23 -0300 Subject: [PATCH 18/21] add news --- NEWS.md | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 NEWS.md diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..c8be0fb --- /dev/null +++ b/NEWS.md @@ -0,0 +1,6 @@ +# DemoKin 1.0.0 + +* Added a `NEWS.md` file to track changes to the package. +* Change stable/non-stable references to time varying/non-varying rates +* Add multi-state process. + From 56e61495c8d9e67a95b7b8e997f23b2d7b4c2406 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Thu, 8 Sep 2022 16:51:21 -0300 Subject: [PATCH 19/21] typo news --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index c8be0fb..1db9e6f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,6 @@ # DemoKin 1.0.0 * Added a `NEWS.md` file to track changes to the package. -* Change stable/non-stable references to time varying/non-varying rates +* Change stable/non-stable references to time varying/non-varying rates. * Add multi-state process. From 99c80490f1bc9cded7d0147803c6a9b34270e4bb Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Fri, 4 Nov 2022 09:34:47 -0300 Subject: [PATCH 20/21] depend forcats --- DESCRIPTION | 4 +++- R/kin.R | 1 + data/swe_surv.rda | Bin 81904 -> 0 bytes man/kin.Rd | 2 ++ vignettes/Reference.Rmd | 10 ++++++---- 5 files changed, 12 insertions(+), 5 deletions(-) delete mode 100644 data/swe_surv.rda diff --git a/DESCRIPTION b/DESCRIPTION index f14db16..9b3e9db 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,12 +14,14 @@ RoxygenNote: 7.2.1 Suggests: knitr, rmarkdown, - testthat (>= 3.0.0) + testthat (>= 3.0.0), + ggplot2 VignetteBuilder: knitr Imports: dplyr, tidyr, purrr, + forcats, HMDHFDplus, progress, matrixcalc, diff --git a/R/kin.R b/R/kin.R index e9a5669..b585345 100644 --- a/R/kin.R +++ b/R/kin.R @@ -11,6 +11,7 @@ #' @param output_period integer. Vector of period years for returning results. Should be within input data years range. #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,... #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1. +#' @param stable logic. Deprecated. Use `time_invariant`. #' @return A list with: #' \itemize{ #' \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.} diff --git a/data/swe_surv.rda b/data/swe_surv.rda deleted file mode 100644 index f00af74cc482396fab5d01db97089bdb919011d3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 81904 zcmb@tV{k4^6E=9q$q7$v+qUiG#I|kQwr$(CZQHiF&%68m-ygeOT{S&b-7`H^eN7Jq zHOx2|1k_0t)ff97u>m3mzQ6x(w6~1M2>$W8^J2R5^x6En>pD3r6YxYD;n9^Ip*wI^Qz_A^?d8REz!!* zeyJJ3X6W4g;_m92;{ED5Ypd;j+w*x_x$XVBbG@sx^K#1Op0&N}>b58oxw)!oxzWv3 zU8@$q$-1-d;-S5w~F!jD^iU9-JGcs=*>?6S#Y?Ly5utiAJE+&S58 zVl%h{Qn|TdvAvwUZtAdotMhzv>v^)>ZR2uzGg_^4(~P}+bH9AO!+2Ta%Amco`gi@% zy0Yb^edjsb;&S7W=jOG&#nZjjxqIWqbG4|m`%yZ{tYjd@I*Si## z_tp94;CZLJwZju4rDmtoaJwYF5Jv(b8SZTrS%MRRjw 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a/man/kin.Rd b/man/kin.Rd index 21c8115..bccb33e 100644 --- a/man/kin.Rd +++ b/man/kin.Rd @@ -35,6 +35,8 @@ kin( \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...} \item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.} + +\item{stable}{logic. Deprecated. Use \code{time_invariant}.} } \value{ A list with: diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd index 388a072..33560b2 100644 --- a/vignettes/Reference.Rmd +++ b/vignettes/Reference.Rmd @@ -11,7 +11,7 @@ vignette: > --- ```{r, include=FALSE} -devtools::load_all() +knitr::opts_chunk$set(collapse = TRUE, comment = "#>") ``` In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country. @@ -25,7 +25,9 @@ In order to implement the time-invariant models, the function `DemoKin::kin` exp ```{r, message=FALSE, warning=FALSE} library(DemoKin) -library(tidyverse) +library(tidyr) +library(dplyr) +library(ggplot2) library(knitr) # First, get vectors for a given year swe_surv_2015 <- swe_px[,"2015"] @@ -245,7 +247,7 @@ demokin_svk1980_caswell2020 %>% filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% mutate(parity = as.integer(stage_kin)-1, parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), - parity = fct_rev(parity)) %>% + parity = forcats::fct_rev(parity)) %>% group_by(age_focal, age_kin, parity) %>% summarise(count= sum(living)) %>% ggplot() + @@ -263,7 +265,7 @@ demokin_svk1980_caswell2020 %>% filter(kin %in% c("d","m")) %>% mutate(parity = as.integer(stage_kin)-1, parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), - parity = fct_rev(parity)) %>% + parity = forcats::fct_rev(parity)) %>% group_by(age_focal, kin, parity) %>% summarise(count= sum(living)) %>% DemoKin::rename_kin() %>% From 76df24339c026e783594a366b08a4d239ec41fd1 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Sat, 19 Nov 2022 10:46:24 -0300 Subject: [PATCH 21/21] fix no pi or N case --- R/kin_time_variant.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index bc962fc..a48306b 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -42,8 +42,10 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, if(is.null(N)){ # create pi and fill it during the loop message("Stable assumption was made for calculating pi on each year because no input data.") + pi_N_null_flag <- TRUE pi <- matrix(0, nrow=ages, ncol=n_years_data) }else{ + pi_N_null_flag <-FALSE pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data))) } } @@ -72,7 +74,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, # print(iyear) Ut <- as.matrix(U[[iyear]]) ft <- as.matrix(f[[iyear]]) - if(is.null(pi)){ + if(pi_N_null_flag){ A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages] A_decomp = eigen(A) w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))

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--git a/dev/kin_multi_stage.R b/dev/kin_multi_stage.R deleted file mode 100644 index 7a89e90..0000000 --- a/dev/kin_multi_stage.R +++ /dev/null @@ -1,223 +0,0 @@ - -# replicate multi-satage paper --------------------------------------------------------------- - -# input and output for testing dowloaded from http://www.demographic-research.org/Volumes/Vol42/38/ - -# # libraries -# library(matrixcalc) -# library(Matrix) -# library(MASS) -# library(tidyverse) -# library(R.matlab) - -# select years for testing -# years_run <- c(seq(1960,2014,4),2014) -# -# # apply demokin function (source function starting line ~ 60) -# kin_out_1960_2014 <- map(years_run, function(year){ -# print(year) -# input_matrix <- readMat(paste0("tests/SVK_kinmats/SVKmats",year-10,".mat")) # Hal has en error in matlab code, is always reading 10 years before -# U <- input_matrix$U -# D <- input_matrix$D -# f <- input_matrix$F -# H <- input_matrix$H -# kin_multi_stage(U, f, D, H, list_output = TRUE) -# }) -# -# # read paper output for same years -# kin_paper_1960_2014 <- map(years_run, function(year){ -# print(year) -# readMat(paste0("tests/SVK_kinout/SVKkinout",year,".mat")) -# }) -# -# # give names both lists -# names(kin_out_1960_2014) <- years_run -# names(kin_paper_1960_2014) <- years_run -# -# # build data frame cheking same output -# check_results <- map_df(names(kin_paper_1960_2014), function(year){ -# -# print(year) -# # how to read matlab files -# d_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,1] -# gd_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,2] -# ggd_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,3] -# m_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,4] -# gm_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,5] -# ggm_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,6] -# os_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,7] -# ys_paper <- kin_paper_1960_2014[[year]]$kinout[,,1]$allkin[,,8] -# -# # output form demokin funtcion -# d_demokin <- kin_out_1960_2014[[as.character(year)]]$d -# gd_demokin <- kin_out_1960_2014[[as.character(year)]]$gd -# ggd_demokin <- kin_out_1960_2014[[as.character(year)]]$ggd -# m_demokin <- kin_out_1960_2014[[as.character(year)]]$m -# gm_demokin <- kin_out_1960_2014[[as.character(year)]]$gm -# ggm_demokin <- kin_out_1960_2014[[as.character(year)]]$ggm -# os_demokin <- kin_out_1960_2014[[as.character(year)]]$os -# ys_demokin <- kin_out_1960_2014[[as.character(year)]]$ys -# -# # logical data frame -# data.frame( -# year = year, -# d = all.equal(d_paper,d_demokin), -# gd = all.equal(gd_paper,gd_demokin), -# ggd = all.equal(ggd_paper,ggd_demokin), -# m = all.equal(m_paper,m_demokin), -# gm = all.equal(gm_paper,gm_demokin), -# ggm = all.equal(ggm_paper,ggm_demokin), -# os = all.equal(os_paper,os_demokin), -# ys = all.equal(ys_paper,ys_demokin)) -# }) -# -# # see same results -# check_results - -# end OK - -# function ---------------------------------------------------------------- - -#' Estimate kin counts by age and stage in a time invariant framework - -#' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). Stages are implied in length of input lists. - -#' @param U numeric. stage transition prob: list of w elements with s x s matrix. -#' @param f numeric. state-specific fertility: list of w elements with s x s matrix. -#' @param D numeric. age advance (depends if U includes survival or not): list of s elements with w x w matrix. -#' @param H numeric. assigns the offspring of individuals in stage j to the appropriate age class: list of s elements with w x w matrix. -#' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types. -#' -#' @return A data frame or a list with ... -#' @export -#' - -kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, - birth_female = 1, - output_kin = NULL, - list_output = FALSE){ - - # stages and ages - s <- length(D) - ages <- length(U) - age <- (1:ages)-1 - - # build block matrix - bbU <- as.matrix(bdiag(lapply(U, function(x) as.matrix(x[[1]])))) - bbF <- as.matrix(bdiag(lapply(f, function(x) as.matrix(x[[1]])))) * birth_female - bbD <- as.matrix(bdiag(lapply(D, function(x) as.matrix(x[[1]])))) - bbH <- as.matrix(bdiag(lapply(H, function(x) as.matrix(x[[1]])))) - - # rearrange amtrix - K <- commutation.matrix(s, ages) - Ut <- t(K) %*% bbD %*% K %*% bbU - ft <- t(K) %*% bbH %*% K %*% bbF - Gt <- Ut%*% ginv(diag(colSums(Ut))) - - # stable distribution mothers: age x stage - At <- Ut+ft - A_decomp <- eigen(At) - lambda <- as.double(A_decomp$values[1]) - wt <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1])) - pi <- wt*At[1,]/sum(wt*At[1,]) - - # marginal mothers age - Iom <- diag(1,ages, ages); - ones <- t(rep(1,s)) - piage <- kronecker(Iom,ones) %*% pi - - # initial vectors - phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*s,ages) - phi[1,1] = 1 - - # momarray is an array with pit in each column - momarray <- pi %*% matrix(1,1,ages) - Iom = diag(1, ages) - Is = diag(1, s) - Isom = diag(1, s*ages) - Z=Is; - Z[1,1]=0; - for(i in 1:ages){ - # imom = 1 - E <- Iom[,i] %*% t(Iom[i,]); # al cuadrado? - momarray[,i] <- kronecker(E,Z) %*% momarray[,i] - } - # re-scale - momarray <- momarray %*% ginv(diag(colSums(momarray))) - - # focal´s trip - m[,1] = momarray %*% piage; - for(i in 1:(ages-1)){ - phi[,i+1] = Gt %*% phi[,i] - d[,i+1] = Ut %*% d[,i] + ft %*% phi[,i] - gd[,i+1] = Ut %*% gd[,i] + ft %*% d[,i] - ggd[,i+1] = Ut %*% ggd[,i] + ft %*% gd[,i] - m[,i+1] = Ut %*% m[,i] - ys[,i+1] = Ut %*% ys[,i] + ft %*% m[,i] - nys[,i+1] = Ut %*% nys[,i] + ft %*% ys[,i] - } - - gm[,1] = m %*% piage - for(i in 1:(ages-1)){ - gm[,i+1] = Ut %*% gm[,i] - } - - ggm[,1] = gm %*% piage - for(i in 1:(ages-1)){ - ggm[,i+1] = Ut %*% ggm[,i] - } - - os[,1] = d %*% piage - nos[1:ages,1] = gd[1:ages,] %*% piage - for(i in 1:(ages-1)){ - os[,i+1] = Ut %*% os[,i] - nos[,i+1] = Ut %*% nos[,i] + ft %*% os[,i] - } - - oa[,1] = os %*% piage - ya[,1] = ys %*% piage - coa[,1] = nos %*% piage - cya[,1] = nys %*% piage - for(i in 1:(ages-1)){ - oa[,i+1] = Ut %*% oa[,i] - ya[,i+1] = Ut %*% ya[,i] + ft %*% gm[,i] - coa[,i+1] = Ut %*% coa[,i] + ft %*% oa[,i] - cya[,i+1] = Ut %*% cya[,i] + ft %*% ya[,i] - } - - # get results - kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys, - nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya) - - # only selected kin - if(!is.null(output_kin)){ - kin_list <- kin_list %>% keep(names(.) %in% output_kin) - } - - # as data.frame - kin <- map2(kin_list, names(kin_list), - function(x,y){ - # y <- "m" - # x <- kin_list[["m"]] - out <- as.data.frame(x) - colnames(out) <- age - out %>% - mutate(kin = y, - age_kin = sort(rep(age,s)), - stage_kin = rep(1:s,ages), - alive = rep("yes",s*ages)) %>% - pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% - mutate(age_focal = as.integer(age_focal)) - } - ) %>% - reduce(rbind) - - # results as list? - if(list_output) { - out <- kin_list - }else{ - out <- kin - } - - return(out) -} diff --git a/man/kin.Rd b/man/kin.Rd index 52cf0a3..eeaa870 100644 --- a/man/kin.Rd +++ b/man/kin.Rd @@ -13,8 +13,7 @@ kin( output_cohort = NULL, output_period = NULL, output_kin = NULL, - birth_female = 1/2.04, - living = TRUE + birth_female = 1/2.04 ) } \arguments{ @@ -22,7 +21,7 @@ kin( \item{f}{numeric. Same as U but for fertility rates.} -\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Defautk \code{TRUE}.} +\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.} \item{N}{numeric. Same as U but for population distribution (counts or \verb{\%}). Optional.} @@ -35,21 +34,24 @@ kin( \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...} \item{birth_female}{numeric. Female portion at birth.} - -\item{living}{logical. Only living kin counts \code{TRUE}, or including death kin \code{FALSE}.} } \value{ A list with: \itemize{ -\item \code{kin}: a data frame with focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.). The content (living or deaths) dependes on \code{alive} param. -\item A data frame with kin at each age of focal´s life. The content (living or deaths) dependes on \code{alive} param. -\item A data frame with kin at actual age of focal. The content (living or deaths) dependes on \code{alive} param. -\item Mean age of each type of kin at actual age of focal. The content (living or deaths) dependes on \code{alive} param. -\item Total of each type of kin at actual age of focal. The content (living or deaths) dependes on \code{alive} param. +\item \code{kin_full}: a data frame with focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age. +\item \code{kin_summary}: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}: +} +\itemize{ +\item \code{count}: count of living kin at actual age of focal +\item \code{mean_age}: mean age of each type of living kin. +\item \code{sd_age}: standard deviation age of each type of living kin . +\item \code{count_death}: count of death kin at specific age of focal. +\item \code{count_cum_death}: cumulated count of death kin at specific age of focal. +\item \code{mean_age_lost}: mean age where focal lost her relative. } } \description{ -Implementation of Goodman-Keyfitz-Pullum equations in a stable or not framework. +Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. } \details{ See Caswell (2019) and Caswell (2021) for details on formulas. diff --git a/man/kin_multi_stage.Rd b/man/kin_multi_stage.Rd new file mode 100644 index 0000000..e375b5f --- /dev/null +++ b/man/kin_multi_stage.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kin_multi_stage.R +\name{kin_multi_stage} +\alias{kin_multi_stage} +\title{Estimate kin counts by age and stage in a time invariant framework} +\usage{ +kin_multi_stage( + U = NULL, + f = NULL, + D = NULL, + H = NULL, + birth_female = 1, + output_kin = NULL, + list_output = FALSE +) +} +\arguments{ +\item{U}{list. age elemnts with column-stochastic transition matrix with dimension for the state space, conditional on survival.} + +\item{f}{matrix. state-specific fertility (age in rows and states in columns).} + +\item{D}{matrix. survival probabilities by state (age in rows and states in columns)} + +\item{H}{matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns).} + +\item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.} + +\item{list_output}{logical. Results as a list. Default \code{FALSE}.} +} +\value{ +A data frame with focal´s age, related ages and type of kin +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death, and specific stage. If \code{list_output = TRUE} then this is a list. +} +\description{ +Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). Stages are implied in length of input lists. +} diff --git a/man/kin_svk1990_caswell2020.Rd b/man/kin_svk1990_caswell2020.Rd new file mode 100644 index 0000000..8bdaf0d --- /dev/null +++ b/man/kin_svk1990_caswell2020.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{kin_svk1990_caswell2020} +\alias{kin_svk1990_caswell2020} +\title{Output for Slovakia 1990 in Caswell (2020).} +\format{ +A list with specific kin types age-stage matrix +} +\source{ +Caswell (2021) +} +\usage{ +kin_svk1990_caswell2020 +} +\description{ +Output for Slovakia 1990 in Caswell (2020). +} +\keyword{datasets} diff --git a/man/kin_time_invariant.Rd b/man/kin_time_invariant.Rd index a40fa34..a470503 100644 --- a/man/kin_time_invariant.Rd +++ b/man/kin_time_invariant.Rd @@ -10,12 +10,11 @@ kin_time_invariant( birth_female = 1/2.04, pi = NULL, output_kin = NULL, - living = TRUE, list_output = FALSE ) } \arguments{ -\item{U}{numeric. A vector of survival ratios with same length as ages.} +\item{U}{numeric. A vector of survival probabilities with same length as ages.} \item{f}{numeric. A vector of age-specific fertility rates with same length as ages.} @@ -25,13 +24,11 @@ kin_time_invariant( \item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.} -\item{living}{logical. Only living kin counts \code{TRUE}, or including death kin \code{FALSE}.} - -\item{list_output}{logical. Results as a list. Default \code{FALSE}} +\item{list_output}{logical. Results as a list with \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}} } \value{ A data frame with focal´s age, related ages and type of kin -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. If \code{list_output = TRUE} then this is a list. } \description{ Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd index 2acfe54..1951eb5 100644 --- a/man/kin_time_variant.Rd +++ b/man/kin_time_variant.Rd @@ -12,7 +12,8 @@ kin_time_variant( output_cohort = NULL, output_period = NULL, output_kin = NULL, - birth_female = 1/2.04 + birth_female = 1/2.04, + list_output = FALSE ) } \arguments{ @@ -31,10 +32,12 @@ kin_time_variant( \item{output_kin}{character. kin to return as output: "m" for mother, "d" for daughter,... See \code{vignette} for exahustive kin.} \item{birth_female}{numeric. Female portion at birth.} + +\item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}} } \value{ A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), and age of (living or deceased) relatives. +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), and age of (living or deceased) relatives. If \code{list_output = TRUE} then this is a list. } \description{ Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021). diff --git a/man/output_period_cohort_combination.Rd b/man/output_period_cohort_combination.Rd new file mode 100644 index 0000000..5b20baf --- /dev/null +++ b/man/output_period_cohort_combination.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kin_time_variant.R +\name{output_period_cohort_combination} +\alias{output_period_cohort_combination} +\title{defince apc combination to return} +\usage{ +output_period_cohort_combination( + output_cohort = NULL, + output_period = NULL, + age = NULL, + years_data = NULL +) +} +\description{ +defince apc to return. +} diff --git a/man/svk_Hxs.Rd b/man/svk_Hxs.Rd new file mode 100644 index 0000000..e6defad --- /dev/null +++ b/man/svk_Hxs.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{svk_Hxs} +\alias{svk_Hxs} +\title{Age where assign offspring of individuals in each partity stage (Caswell, 2021). All to zero age in this case.} +\format{ +A matrix of ones in ages where assign offspring individuals, with stages as cols and ages as rows. +} +\source{ +Caswell (2021) +} +\usage{ +svk_Hxs +} +\description{ +Age where assign offspring of individuals in each partity stage (Caswell, 2021). All to zero age in this case. +} +\keyword{datasets} diff --git a/man/svk_Uxs.Rd b/man/svk_Uxs.Rd new file mode 100644 index 0000000..5e17752 --- /dev/null +++ b/man/svk_Uxs.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{svk_Uxs} +\alias{svk_Uxs} +\title{Probability of transition among parity stage for Slovakia in 1990, for each age, conditional on survival (Caswell, 2021).} +\format{ +A list of column-stochastic matrix with probabilities of transition among parity stage, for each age, conditional on survival. +} +\source{ +Caswell (2021) +} +\usage{ +svk_Uxs +} +\description{ +Probability of transition among parity stage for Slovakia in 1990, for each age, conditional on survival (Caswell, 2021). +} +\keyword{datasets} diff --git a/man/svk_fxs.Rd b/man/svk_fxs.Rd new file mode 100644 index 0000000..7a788af --- /dev/null +++ b/man/svk_fxs.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{svk_fxs} +\alias{svk_fxs} +\title{Female Slovakian fertility rates by parity stage in 1990 (Caswell, 2021)} +\format{ +A matrix of fx with stages as cols and ages as rows. +} +\source{ +Caswell (2021) +} +\usage{ +svk_fxs +} +\description{ +Female Slovakian fertility rates by parity stage in 1990 (Caswell, 2021) +} +\keyword{datasets} diff --git a/man/svk_pxs.Rd b/man/svk_pxs.Rd new file mode 100644 index 0000000..99b25ed --- /dev/null +++ b/man/svk_pxs.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{svk_pxs} +\alias{svk_pxs} +\title{Female Slovakian survival probabilities by parity stage in 1990 (Caswell, 2021)} +\format{ +A matrix of px with stages as cols and ages as rows. +} +\source{ +Caswell (2021) +} +\usage{ +svk_pxs +} +\description{ +Female Slovakian survival probabilities by parity stage in 1990 (Caswell, 2021) +} +\keyword{datasets} diff --git a/man/timevarying_kin.Rd b/man/timevarying_kin.Rd index 66e8b5d..1826543 100644 --- a/man/timevarying_kin.Rd +++ b/man/timevarying_kin.Rd @@ -7,15 +7,15 @@ timevarying_kin(Ut, ft, pit, ages, pkin) } \arguments{ -\item{Ut}{numeric. A matrix of survival ratios with rows as ages and columns as years. The name of each col must be the year.} +\item{Ut}{numeric. A matrix of survival probabilities (or ratios).} -\item{ft}{numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. The name of each col must be the year.} +\item{ft}{numeric. A matrix of age-specific fertility rates.} + +\item{pit}{numeric. A matrix with distribution of childbearing.} \item{ages}{numeric.} \item{pkin}{numeric. A list with kin count distribution in previous year.} - -\item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. The name of each col must be the year.} } \description{ one time projection kin. internal function. diff --git a/tests/testthat.R b/tests/testthat.R new file mode 100644 index 0000000..069f591 --- /dev/null +++ b/tests/testthat.R @@ -0,0 +1,12 @@ +# This file is part of the standard setup for testthat. +# It is recommended that you do not modify it. +# +# Where should you do additional test configuration? +# Learn more about the roles of various files in: +# * https://r-pkgs.org/tests.html +# * https://testthat.r-lib.org/reference/test_package.html#special-files + +library(testthat) +library(DemoKin) + +test_check("DemoKin") diff --git a/tests/testthat/test-kin_multi_stage.R b/tests/testthat/test-kin_multi_stage.R new file mode 100644 index 0000000..2e3d58d --- /dev/null +++ b/tests/testthat/test-kin_multi_stage.R @@ -0,0 +1,20 @@ +# test multi-stage function against caswell 2020 + +test_that("same output in multi_stage (caswell 2020)", { + demokin_svk1990_caswell2020 <- kin_multi_stage(U = svk_Uxs, + f = svk_fxs, + D = svk_pxs, + H = svk_Hxs, birth_female=1, list_output = TRUE) + expect_equal(demokin_svk1990_caswell2020$d[1:(110*6),], kin_svk1990_caswell2020$d) + expect_equal(demokin_svk1990_caswell2020$gd[1:(110*6),], kin_svk1990_caswell2020$gd) + expect_equal(demokin_svk1990_caswell2020$ggd[1:(110*6),], kin_svk1990_caswell2020$ggd) + expect_equal(demokin_svk1990_caswell2020$m[1:(110*6),], kin_svk1990_caswell2020$m) + expect_equal(demokin_svk1990_caswell2020$gm[1:(110*6),], kin_svk1990_caswell2020$gm) + expect_equal(demokin_svk1990_caswell2020$ggm[1:(110*6),], kin_svk1990_caswell2020$ggm) + expect_equal(demokin_svk1990_caswell2020$os[1:(110*6),], kin_svk1990_caswell2020$os) + expect_equal(demokin_svk1990_caswell2020$ys[1:(110*6),], kin_svk1990_caswell2020$ys) +}) + + + + diff --git a/vignettes/Use.Rmd b/vignettes/Use.Rmd index 2ff429a..fa127a3 100644 --- a/vignettes/Use.Rmd +++ b/vignettes/Use.Rmd @@ -12,9 +12,7 @@ vignette: > ```{r, include=FALSE} devtools::load_all() library(tidyverse) -library(DiagrammeR) library(knitr) -library(progress) ``` The year is 2015 and focal is 35 years old^[Age should be understood as a class state, defined in a matrix transition framework.]. Focal and her relatives have all lived in Sweden. @@ -68,9 +66,10 @@ We can also visualize the age distribution of relatives when focal is 35 for exa ```{r, fig.height=6, fig.width=8} swe_2015[["kin_full"]] %>% - filter(age_focal == 35) %>% + filter(alive=="yes", age_focal == 35) %>% ggplot() + geom_line(aes(age_kin, count)) + + geom_vline(xintercept = 35, color=2) + theme_bw() + facet_wrap(~kin) ``` @@ -108,6 +107,7 @@ swe_2015_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invaria output_kin = c("d","gd","ggd","m","gm","ggm")) swe_2015_time_varying$kin_full %>% + filter(alive=="yes") %>% group_by(kin, cohort, age_focal) %>% summarise(count = sum(count)) %>% ggplot(aes(age_focal,count,color=factor(cohort))) + scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ @@ -127,13 +127,11 @@ Kin loss has studied consequences related to mental health, care support and hou ```{r, fig.height=6, fig.width=8} swe_1900_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant = FALSE, output_cohort = 1900, - output_kin = c("d","gd","ggd","m","gm","ggm"), - living = FALSE) + output_kin = c("d","gd","ggd","m","gm","ggm")) swe_1900_time_varying$kin_summary %>% - filter(alive =="no") %>% ggplot() + - geom_line(aes(age_focal, count_cum)) + + geom_line(aes(age_focal, count_cum_death)) + theme_bw() + facet_wrap(~kin,scales="free") ``` @@ -142,15 +140,15 @@ Given some portion of each relative was dead (remember we are talking about popu ```{r} swe_1900_time_varying$kin_summary %>% - filter(alive=="no", age_focal == 50) %>% - select(kin,count_cum,mean_age_lost) %>% + filter(age_focal == 50) %>% + select(kin,count_cum_death,mean_age_lost) %>% mutate_if(is.numeric, round, 2) %>% kable() ``` ## Prevalences -Once you have a full kin distribution by age, is possible to know, for example given a set of prevalence by age, what is the expected portion of living kin in each prevalence state (could be disease, employment, etc.). +Once you have a full kin distribution by age, is possible to know what is the expected portion of living kin in some stage given a set of prevalence by age (could be a disease, employment, etc.). This kind of method is known as Sullivan in life table apporaches. A matrix formulation for same results can be found in Caswell (2019). This can be applied for time variant prevalence too. ```{r} # let´s create some prevalence shcedule by age @@ -174,12 +172,61 @@ swe_2015$kin_full %>% ``` -This can be applied for time variant cases too. +## Multi-state + +In `DemoKin` is possible to obtain kin structures in a multistate framework, classifying individuals jointly by age and other feature (like stages of some disease). For that is neccesary to use mortality and fertility for each possible stage (conditionated to), indicate in which age and state the new individuals (offspring) woul be destinated to,and the probabilities for changing state by age. + +Let´s take the Caswell (2021) example for Slovakia where stages are parity states. The package presents data for the year 1980, where `svk_fxs` is the fertility rate by age (rows) for each parity stage (columns). The stage 1 is related to no kids (parity zero), the 2 is 1, and so on, until finally the 6 is 5 or more. The data.frame `svk_Hxs` has same structure but with $1$ in the age where newborns are destinated, in this case always to first age. The data.frame `svk_pxs` has same structure but with survivial probabilities. Finally we need a list `svk_Uxs` that has same number of elements than ages (in this case 110, where $omega$ is 109), and for each age there is a column-stochastic transition matrix with dimension for the state space, and the entries are transition probabilities conditional on survival. Based on Caswell (2020) matrix methods is possible to know joint age-parity kin structure. + +```{r} +# use birth_female=1 because fertility is for female only +demokin_svk1980_caswell2020 <- kin_multi_stage(U = svk_Uxs, + f = svk_fxs, + D = svk_pxs, + H = svk_Hxs, birth_female=1) + + +``` + +We can see age-parity distribution of aunts, when focal is 20 or 60 (similar to figure 4 in Caswell(2021)). + +```{r} +demokin_svk1980_caswell2020 %>% + filter(kin %in% c("oa","ya"), alive == "yes", age_focal %in% c(20,60)) %>% + mutate(parity = as.integer(stage_kin)-1, + parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), + parity = fct_rev(parity)) %>% + group_by(age_focal, age_kin, parity) %>% + summarise(count= sum(count)) %>% + ggplot() + + geom_bar(aes(x=age_kin, y = count, fill=parity), stat = "identity") + + geom_vline(aes(xintercept = age_focal), col=2) + + theme_bw() + + facet_wrap(~age_focal, nrow = 2) +``` + +Also we can see, When focal grows up, the portion of living daughters and mothers in different parity stages (similar to figure 9 and 10 in Caswell(2021)). + +```{r} +demokin_svk1980_caswell2020 %>% + filter(kin %in% c("d","m"), alive == "yes") %>% + mutate(parity = as.integer(stage_kin)-1, + parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), + parity = fct_rev(parity)) %>% + group_by(age_focal, kin, parity) %>% + summarise(count= sum(count)) %>% + ggplot() + + geom_bar(aes(x=age_focal, y = count, fill=parity), stat = "identity") + + theme_bw() + + facet_wrap(~kin, nrow = 2) +``` ## References Caswell, H. (2019). The formal demography of kinhip: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24. +Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097–1146. + Caswell, H., & Song, X. (2021). The formal demography of kinhip III: kinhip dynamics with time-varying demographic rates. Demographic Research, 45, 517–546. Preston, S., Heuveline, P., & Guillot, M. (2000). Demography: Measuring and Modeling Population Processes. Wiley. From 7a9e9b261bdf3083689c9d1a552d155a452c0740 Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Tue, 30 Aug 2022 08:45:18 -0300 Subject: [PATCH 06/21] living wider in kin_full --- R/kin.R | 20 +++++++++----------- R/kin_multi_stage.R | 5 +++-- R/kin_time_invariant.R | 5 +++-- R/kin_time_variant.R | 9 +++++---- 4 files changed, 20 insertions(+), 19 deletions(-) diff --git a/R/kin.R b/R/kin.R index 4c14cc3..dfe656b 100644 --- a/R/kin.R +++ b/R/kin.R @@ -52,6 +52,7 @@ kin <- function(U = NULL, f = NULL, output_kin = output_kin, birth_female = birth_female) %>% dplyr::mutate(cohort = NA, year = NA) }else{ + if(!is.null(output_cohort) & !is.null(output_period)) stop("sorry, you can not select cohort and period. Choose one please") kin_full <- kin_time_variant(U = U, f = f, N = N, pi = pi, output_cohort = output_cohort, output_period = output_period, output_kin = output_kin, @@ -60,29 +61,26 @@ kin <- function(U = NULL, f = NULL, } # reorder - kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, alive, count) + kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, living, death) # summary kin_summary <- dplyr::bind_rows( kin_full %>% - dplyr::filter(alive=="yes") %>% - dplyr::rename(total=count) %>% - dplyr::group_by(cohort, alive, age_focal, kin) %>% + dplyr::rename(total=living) %>% + dplyr::group_by(cohort, year, age_focal, kin) %>% dplyr::summarise(count = sum(total), mean_age = sum(total*age_kin)/sum(total), sd_age = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>% tidyr::pivot_longer(count:sd_age, names_to = "indicator", "value"), kin_full %>% - dplyr::filter(alive=="no") %>% - dplyr::rename(total=count) %>% - dplyr::group_by(cohort, alive, age_focal, kin) %>% + dplyr::rename(total=death) %>% + dplyr::group_by(cohort, year, age_focal, kin) %>% dplyr::summarise(count_death = sum(total)) %>% - dplyr::group_by(cohort, alive, kin) %>% + dplyr::group_by(cohort, year, kin) %>% dplyr::mutate(count_cum_death = cumsum(count_death), - mean_age_lost = cumsum(count_death*age_focal)/cumsum(count_death)) %>% + mean_age_lost = cumsum(count_death * age_focal)/cumsum(count_death)) %>% dplyr::ungroup() %>% - tidyr::pivot_longer(count_death:mean_age_lost, names_to = "indicator", "value") - ) %>% + tidyr::pivot_longer(count_death:mean_age_lost, names_to = "indicator", "value")) %>% dplyr::ungroup() %>% dplyr::select(cohort, age_focal, kin, indicator, value) %>% tidyr::pivot_wider(names_from = indicator, values_from = value) diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R index fa70665..2335dd1 100644 --- a/R/kin_multi_stage.R +++ b/R/kin_multi_stage.R @@ -161,13 +161,14 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL, dplyr::mutate(kin = y, age_kin = rep(sort(rep(age,s)),2), stage_kin = rep(rep(1:s,ages),2), - alive = c(rep("yes",s*ages),rep("no",s*ages)) + alive = c(rep("living",s*ages),rep("death",s*ages)) # age_kin = sort(rep(age,s)), # stage_kin = rep(1:s,ages), # alive = c(rep("yes",s*ages)) ) %>% tidyr::pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% - dplyr::mutate(age_focal = as.integer(age_focal)) + dplyr::mutate(age_focal = as.integer(age_focal)) %>% + tidyr::pivot_wider(names_from = alive, values_from = count) }) %>% purrr::reduce(rbind) diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R index b390268..23df06d 100644 --- a/R/kin_time_invariant.R +++ b/R/kin_time_invariant.R @@ -103,9 +103,10 @@ kin_time_invariant <- function(U = NULL, f = NULL, out %>% dplyr::mutate(kin = y, age_kin = rep(age,2), - alive = c(rep("yes",ages), rep("no",ages))) %>% + alive = c(rep("living",ages), rep("death",ages))) %>% tidyr::pivot_longer(c(-age_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>% - dplyr::mutate(age_focal = as.integer(age_focal)) + dplyr::mutate(age_focal = as.integer(age_focal)) %>% + tidyr::pivot_wider(names_from = alive, values_from = count) } ) %>% purrr::reduce(rbind) diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index 4fc8c4d..1770bc6 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -116,15 +116,16 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, dplyr::mutate(year = Y, kin=y, age_kin = rep(age,2), - alive = c(rep("yes",ages), rep("no",ages)), + alive = c(rep("living",ages), rep("death",ages)), .before=everything())) %>% dplyr::bind_rows() %>% stats::setNames(c("year","kin","age_kin","alive",as.character(age))) %>% tidyr::gather(age_focal, count,-age_kin, -kin, -year, -alive) %>% dplyr::mutate(age_focal = as.integer(age_focal), - year = as.integer(year), - cohort = year - age_focal) %>% - dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)])}) %>% + year = as.integer(year), + cohort = year - age_focal) %>% + dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)]) %>% + tidyr::pivot_wider(names_from = alive, values_from = count)}) %>% dplyr::bind_rows() # results as list? From ae129278711f17ffaa5c55c8311e7b31997ed8bd Mon Sep 17 00:00:00 2001 From: Ivan Williams Date: Tue, 30 Aug 2022 10:47:04 -0300 Subject: [PATCH 07/21] wider kin_full and pre-release upd --- DESCRIPTION | 2 +- NAMESPACE | 2 + R/aux_funs.R | 43 +++++++ R/kin.R | 31 +++-- R/kin_multi_stage.R | 4 +- R/kin_time_variant.R | 2 +- R/plot_diagramm.R | 140 +++++++++++------------ README.Rmd | 56 +++++---- README.md | 73 +++++++----- man/demokin_codes.Rd | 11 ++ man/figures/README-unnamed-chunk-5-1.png | Bin 24513 -> 7562 bytes man/kin.Rd | 8 +- man/kin_multi_stage.Rd | 4 +- man/kin_time_variant.Rd | 2 +- man/plot_diagram.Rd | 2 +- man/rename_kin.Rd | 11 ++ vignettes/{Use.Rmd => Reference.Rmd} | 66 +++++------ 17 files changed, 273 insertions(+), 184 deletions(-) create mode 100644 R/aux_funs.R create mode 100644 man/demokin_codes.Rd create mode 100644 man/rename_kin.Rd rename vignettes/{Use.Rmd => Reference.Rmd} (83%) diff --git a/DESCRIPTION b/DESCRIPTION index 4ba1dc3..9d9e502 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,7 @@ Suggests: rmarkdown, testthat (>= 3.0.0) VignetteBuilder: knitr -Imports: dplyr, tidyr, purrr, DiagrammeR, HMDHFDplus, devtools, progress, matrixcalc, Matrix, MASS, ggplot2, stats +Imports: dplyr, tidyr, purrr, HMDHFDplus, devtools, progress, matrixcalc, Matrix, MASS, ggplot2, stats, igraph, DiagrammeR BugReports: https://github.com/IvanWilli/DemoKin/issues Depends: R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index 3ac5760..513c4db 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,9 +1,11 @@ # Generated by roxygen2: do not edit by hand +export(demokin_codes) export(get_HMDHFD) export(kin) export(kin_multi_stage) export(kin_time_invariant) export(kin_time_variant) export(plot_diagram) +export(rename_kin) importFrom(DiagrammeR,mermaid) diff --git a/R/aux_funs.R b/R/aux_funs.R new file mode 100644 index 0000000..137ff67 --- /dev/null +++ b/R/aux_funs.R @@ -0,0 +1,43 @@ + +#' print kin codes +#' @description Print kin codes and labels +#' @export +demokin_codes <- function(){ + codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") + caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") + labels <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") + data.frame(DemoKin = codes, Caswell = caswell_codes, Label = labels, row.names = NULL) +} + +#' rename kin +#' @description Rename kin labels depending consolidate some types +#' @export +rename_kin <- function(df, consolidate = FALSE){ + + if(!consolidate){ + + relatives <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") + names(relatives) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") + + } else if(consolidate){ + + # Combine kin types irrespective of whether they come from older + # or younger sibling lines + consolidate <- c("c", "c", "d", "gd", "ggd", "ggm", "gm", "m", "n", "n", "a", "a", "s", "s") + names(consolidate) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") + + # Rename kin types from codes to actual words + relatives <- c("Cousins", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces", "Aunt", "Sister") + names(relatives) <- unique(consolidate) + + df <- + df %>% + dplyr::mutate(kin = consolidate[kin]) %>% + dplyr::group_by(age_focal, kin) %>% + dplyr::summarise(count = sum(count)) %>% + dplyr::ungroup() + + } + df$kin <- relatives[df$kin] + df +} diff --git a/R/kin.R b/R/kin.R index dfe656b..3ceed5a 100644 --- a/R/kin.R +++ b/R/kin.R @@ -12,13 +12,13 @@ #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,... #' @param birth_female numeric. Female portion at birth. #' @return A list with: -#' * `kin_full`: a data frame with focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age. -#' * `kin_summary`: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`: -#' - `count`: count of living kin at actual age of focal +#' * `kin_full`: a data frame with year, cohort, focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), with living kin and death on that age. +#' * `kin_summary`: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending arguments): +#' - `count_living`: count of living kin at actual age of focal #' - `mean_age`: mean age of each type of living kin. #' - `sd_age`: standard deviation age of each type of living kin . #' - `count_death`: count of death kin at specific age of focal. -#' - `count_cum_death`: cumulated count of death kin at specific age of focal. +#' - `count_cum_death`: cumulated count of death kin until specific age of focal. #' - `mean_age_lost`: mean age where focal lost her relative. #' @export #' @@ -64,25 +64,36 @@ kin <- function(U = NULL, f = NULL, kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, living, death) # summary + # select period/cohort + if(!is.null(output_cohort)){ + agrupar <- "cohort" + } else if(!is.null(output_period)){ + agrupar <- "year" + } else{ + agrupar <- c("year", "cohort") + } + agrupar_no_age_focal <- c("kin", agrupar) + agrupar <- c("age_focal", "kin", agrupar) + kin_summary <- dplyr::bind_rows( kin_full %>% dplyr::rename(total=living) %>% - dplyr::group_by(cohort, year, age_focal, kin) %>% - dplyr::summarise(count = sum(total), + dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>% + dplyr::summarise(count_living = sum(total), mean_age = sum(total*age_kin)/sum(total), sd_age = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>% - tidyr::pivot_longer(count:sd_age, names_to = "indicator", "value"), + tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"), kin_full %>% dplyr::rename(total=death) %>% - dplyr::group_by(cohort, year, age_focal, kin) %>% + dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>% dplyr::summarise(count_death = sum(total)) %>% - dplyr::group_by(cohort, year, kin) %>% + dplyr::ungroup() %>% + dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>% dplyr::mutate(count_cum_death = cumsum(count_death), mean_age_lost = cumsum(count_death * age_focal)/cumsum(count_death)) %>% dplyr::ungroup() %>% tidyr::pivot_longer(count_death:mean_age_lost, names_to = "indicator", "value")) %>% dplyr::ungroup() %>% - dplyr::select(cohort, age_focal, kin, indicator, value) %>% tidyr::pivot_wider(names_from = indicator, values_from = value) # return diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R index 2335dd1..ccb0a89 100644 --- a/R/kin_multi_stage.R +++ b/R/kin_multi_stage.R @@ -1,6 +1,6 @@ #' Estimate kin counts by age and stage in a time invariant framework -#' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). Stages are implied in length of input lists. +#' @description Implementation of age-stage kin estimates (multi-state) by Caswell (2020). Stages are implied in length of input lists. #' @param U list. age elemnts with column-stochastic transition matrix with dimension for the state space, conditional on survival. #' @param f matrix. state-specific fertility (age in rows and states in columns). @@ -10,7 +10,7 @@ #' @param list_output logical. Results as a list. Default `FALSE`. #' @return A data frame with focal´s age, related ages and type of kin -#' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death, and specific stage. If `list_output = TRUE` then this is a list. +#' (for example `d` is daughter, `oa` is older aunts, etc.), living and death kin counts, and specific stage. If `list_output = TRUE` then this is a list with elements as kin types. #' @export #' diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R index 1770bc6..ff69a19 100644 --- a/R/kin_time_variant.R +++ b/R/kin_time_variant.R @@ -13,7 +13,7 @@ #' @param list_output logical. Results as a list with years elements (as a result of `output_cohort` and `output_period` combination), with a second list of `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE` #' @return A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives -#' (for example `d` is daughter, `oa` is older aunts, etc.), and age of (living or deceased) relatives. If `list_output = TRUE` then this is a list. +#' (for example `d` is daughter, `oa` is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If `list_output = TRUE` then this is a list. #' @export kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL, diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R index 35d752d..1604108 100644 --- a/R/plot_diagramm.R +++ b/R/plot_diagramm.R @@ -1,91 +1,91 @@ #' plot a Kin diagram (network) #' @description Given estimation of kin counts from `kins` function, draw a diagramm. -#' @param kins_total vector. Colum names must be as `kins` output. +#' @param kins_total data.frame. kin type and some count in columns. #' @param rounding numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm. #' @return A plot #' @importFrom DiagrammeR mermaid #' @export plot_diagram <- function(kin_total, rounding = 3){ - # https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html - # https://color.hailpixel.com/#D9E9BE,BF62CB,94C2DB,79D297,CDA76A,C8695B + library(igraph) + vertices <- data.frame( + nodes = c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys") + , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3) + , y = c(0, 1, 2, 3, 4, 5, 6, 4, 3, 3, 2, 4, 3, 3, 2) + ) - kin_total <- kin_total %>% mutate(count = round(count,digits = rounding)) + d <- data.frame( + from = c("ggd", "gd", "d", "Focal", "m", "gm", "gm", "oa", "m", "os", "gm", "ya", "m", "ys") + , to = c("gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys") + ) - DiagrammeR::mermaid( - paste0("graph TD + # Add values + lookup <- c(with(kin_total, paste0(kin, " \n", round(count, rounding))), "Focal") + names(lookup) <- c(kin_total$kin, "Focal") + + vertices$nodes <- lookup[vertices$nodes] + d$from <- lookup[d$from] + d$to <- lookup[d$to] + + # Plot + + b <- igraph::graph_from_data_frame(vertices = vertices, d= d, directed = FALSE) + + plot( + b + , vertex.size = 30 + , curved = 1 + , vertex.color = "#FFF1E2" + , vertex.shape = "circle" + , vertex.label.cex = 0.8 + , vertex.label.color = "black" + , label.degree = -pi/2 + , edge.width = 2 + , edge.color = "black" + ) - GGM(ggm:
    ", kin_total$count[kin_total$kin=="ggm"] ,") - GGM ==> GM(gm:
    ", kin_total$count[kin_total$kin=="gm"] ,") - GM --> AOM(oa:
    ", kin_total$count[kin_total$kin=="oa"] ,") - GM ==> M(m:
    ", kin_total$count[kin_total$kin=="m"] ,") - GM --> AYM(ya:
    ", kin_total$count[kin_total$kin=="ya"] ,") - AOM --> CAOM(coa:
    ", kin_total$count[kin_total$kin=="coa"] ,") - M --> OS(os:
    ", kin_total$count[kin_total$kin=="os"] ,") - M ==> E((Ego)) - M --> YS(ys:
    ", kin_total$count[kin_total$kin=="ys"] ,") - AYM --> CAYM(cya:
    ", kin_total$count[kin_total$kin=="cya"] ,") - OS --> NOS(nos:
    ", kin_total$count[kin_total$kin=="nos"] ,") - E ==> D(d:
    ", kin_total$count[kin_total$kin=="d"] ,") - YS --> NYS(nys:
    ", kin_total$count[kin_total$kin=="nys"] ,") - D ==> GD(gd:
    ", kin_total$count[kin_total$kin=="gd"] ,") - style GGM fill:#a1f590, stroke:#333, stroke-width:2px; - style GM fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center; - style M fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center - style D fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center - style YS fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center - style OS fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center - style CAOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style AYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style AOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style CAYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style NOS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style NYS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center - style E fill:#FFF, stroke:#333, stroke-width:4px, text-align: center - style D fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center - style GD fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center")) } +# old function -# new diagram: to improve print quality # plot_diagram <- function(kin_total, rounding = 3){ +# # https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html +# # https://color.hailpixel.com/#D9E9BE,BF62CB,94C2DB,79D297,CDA76A,C8695B # -# vertices <- data.frame( -# nodes = c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys") -# , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3) -# , y = c(0, 1, 2, 3, 4, 5, 6, 4, 3, 3, 2, 4, 3, 3, 2) -# ) -# -# d <- data.frame( -# from = c("ggd", "gd", "d", "Focal", "m", "gm", "gm", "oa", "m", "os", "gm", "ya", "m", "ys") -# , to = c("gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys") -# ) -# -# # Add values -# lookup <- c(with(kin_total, paste0(kin, " \n", round(count, rounding))), "Focal") -# names(lookup) <- c(kin_total$kin, "Focal") -# -# vertices$nodes <- lookup[vertices$nodes] -# d$from <- lookup[d$from] -# d$to <- lookup[d$to] -# -# # Plot -# -# b <- graph_from_data_frame(vertices = vertices, d= d, directed = FALSE) +# kin_total <- kin_total %>% mutate(count = round(count,digits = rounding)) # -# plot( -# b -# , vertex.size = 30 -# , curved = 1 -# , vertex.color = "#FFF1E2" -# , vertex.shape = "circle" -# , vertex.label.cex = 0.8 -# , vertex.label.color = "black" -# , label.degree = -pi/2 -# , edge.width = 2 -# , edge.color = "black" -# ) +# DiagrammeR::mermaid( +# paste0("graph TD # +# GGM(ggm:
    ", kin_total$count[kin_total$kin=="ggm"] ,") +# GGM ==> GM(gm:
    ", kin_total$count[kin_total$kin=="gm"] ,") +# GM --> AOM(oa:
    ", kin_total$count[kin_total$kin=="oa"] ,") +# GM ==> M(m:
    ", kin_total$count[kin_total$kin=="m"] ,") +# GM --> AYM(ya:
    ", kin_total$count[kin_total$kin=="ya"] ,") +# AOM --> CAOM(coa:
    ", kin_total$count[kin_total$kin=="coa"] ,") +# M --> OS(os:
    ", kin_total$count[kin_total$kin=="os"] ,") +# M ==> E((Ego)) +# M --> YS(ys:
    ", kin_total$count[kin_total$kin=="ys"] ,") +# AYM --> CAYM(cya:
    ", kin_total$count[kin_total$kin=="cya"] ,") +# OS --> NOS(nos:
    ", kin_total$count[kin_total$kin=="nos"] ,") +# E ==> D(d:
    ", kin_total$count[kin_total$kin=="d"] ,") +# YS --> NYS(nys:
    ", kin_total$count[kin_total$kin=="nys"] ,") +# D ==> GD(gd:
    ", kin_total$count[kin_total$kin=="gd"] ,") +# style GGM fill:#a1f590, stroke:#333, stroke-width:2px; +# style GM fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center; +# style M fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center +# style D fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center +# style YS fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center +# style OS fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center +# style CAOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style AYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style AOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style CAYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style NOS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style NYS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center +# style E fill:#FFF, stroke:#333, stroke-width:4px, text-align: center +# style D fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center +# style GD fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center")) # } diff --git a/README.Rmd b/README.Rmd index 0486f2e..800e3da 100644 --- a/README.Rmd +++ b/README.Rmd @@ -11,11 +11,9 @@ knitr::opts_chunk$set( fig.path = "man/figures/README-", out.width = "100%" ) +devtools::load_all() library(tidyverse) -library(DiagrammeR) library(knitr) -library(igraph) -devtools::load_all() library(DemoKin) ``` @@ -33,24 +31,8 @@ devtools::install_github("IvanWilli/DemoKin") ``` ## Example +Consider an average Swedish woman in year 2015 (i.e., 'Focal'). We assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life. -Consider an average Swedish woman in year 2015 (i.e., 'Focal'). -Relatives for the `selected_kin` argument are identified by a unique code. -Note that the relationship codes used in `DemoKin` differ from those in Caswell (2019). -The equivalence between the two set of codes is given in the following table: - -```{r, fig.height=6, fig.width=8, echo=FALSE} - -codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") -caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") -labels <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") - -data.frame(DemoKin = codes, Caswell = caswell_codes, Label = labels, row.names = NULL) %>% kable(caption = "Equivalence between relative codes between DemoKin and Caswell (2019).") - -``` - -Let's show a quick example. -We assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life. We then ask: > How can we characterize the kinship network of an average member of the population ('Focal')? @@ -66,7 +48,39 @@ swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) *px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). -For more details, including an extension to time varying populations rates and relative´s death distribution, see `vignette("Use")`. +Relatives for the `selected_kin` argument of function `kin` are identified by a unique code. + +```{r, fig.height=6, fig.width=8, echo=FALSE} + +relatives <- c( + "coa" = "Cousins (through aunt older than mother)" + , "cya" = "Cousins (through aunt younger than mother)" + , "d" = "Daughter" + , "gd" = "Grand-daughter" + , "ggm" = "Great-grandmother" + , "gm" = "Grandmother" + , "m" = "Mother" + , "nos" = "Nieces through older sister" + , "nys" = "Nieces through younger sister" + , "oa" = "Aunt older than mother" + , "ya" = "Aunt younger than mother" + , "os" = "Older sister" + , "ys" = "Younger sister" + ) + +data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) %>% kable() +``` + +We can visualize the implied kin counts for a Focal aged 35 in Sweden (with freeze rates from 2015) using a network or 'Keyfitz' kinship diagram using the `plot_diagram` function: + +```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} +plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% + dplyr::filter(age_focal == 35) %>% + dplyr::select(kin, count = count_living), + rounding = 2) +``` + +For more details, including an extension to time varying populations rates, relative´s death distribution and multi-state model, see `vignette("Use")`. Note that if the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation diff --git a/README.md b/README.md index 7feaadb..9ec3158 100644 --- a/README.md +++ b/README.md @@ -20,34 +20,12 @@ devtools::install_github("IvanWilli/DemoKin") ## Example -Consider an average Swedish woman in year 2015 (i.e., ‘Focal’). -Relatives for the `selected_kin` argument are identified by a unique -code. Note that the relationship codes used in `DemoKin` differ from -those in Caswell (2019). The equivalence between the two set of codes is -given in the following table: - -| DemoKin | Caswell | Label | -|:--------|:--------|:---------------------------| -| coa | t | Cousins from older aunt | -| cya | v | Cousins from younger aunt | -| d | a | Daughter | -| gd | b | Grand-daughter | -| ggd | c | Great-grand-daughter | -| ggm | h | Great-grandmother | -| gm | g | Grandmother | -| m | d | Mother | -| nos | p | Nieces from older sister | -| nys | q | Nieces from younger sister | -| oa | r | Aunt older than mother | -| ya | s | Aunt younger than mother | -| os | m | Older sister | -| ys | n | Younger sister | - -Equivalence between relative codes between DemoKin and Caswell (2019). - -Let’s show a quick example. We assume a female closed population in -which everyone experiences the Swedish 2015 mortality and fertility -rates at each age throughout their life. We then ask: +Consider an average Swedish woman in year 2015 (i.e., ‘Focal’). We +assume a female closed population in which everyone experiences the +Swedish 2015 mortality and fertility rates at each age throughout their +life. + +We then ask: > How can we characterize the kinship network of an average member of > the population (‘Focal’)? @@ -64,10 +42,43 @@ swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) *px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). +Relatives for the `selected_kin` argument of function `kin` are +identified by a unique code. + +| Code | Relative | +|:-----|:-------------------------------------------| +| coa | Cousins (through aunt older than mother) | +| cya | Cousins (through aunt younger than mother) | +| d | Daughter | +| gd | Grand-daughter | +| ggm | Great-grandmother | +| gm | Grandmother | +| m | Mother | +| nos | Nieces through older sister | +| nys | Nieces through younger sister | +| oa | Aunt older than mother | +| ya | Aunt younger than mother | +| os | Older sister | +| ys | Younger sister | + +We can visualize the implied kin counts for a Focal aged 35 in Sweden +(with freeze rates from 2015) using a network or ‘Keyfitz’ kinship +diagram using the `plot_diagram` function: + +``` r +plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% + dplyr::filter(age_focal == 35) %>% + dplyr::select(kin, count = count_living), + rounding = 2) +``` + + + For more details, including an extension to time varying populations -rates and relative´s death distribution, see `vignette("Use")`. Note -that if the vignette does not load, you may need to install the package -as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. +rates, relative´s death distribution and multi-state model, see +`vignette("Use")`. Note that if the vignette does not load, you may need +to install the package as +`devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation diff --git a/man/demokin_codes.Rd b/man/demokin_codes.Rd new file mode 100644 index 0000000..deb1b04 --- /dev/null +++ b/man/demokin_codes.Rd @@ -0,0 +1,11 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/aux_funs.R +\name{demokin_codes} +\alias{demokin_codes} +\title{print kin codes} +\usage{ +demokin_codes() +} +\description{ +Print kin codes and labels +} diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png index c780cfc34db949c1d51b57fc0fbc058cb6d147f4..5de1650800e156a3a3a92d990bdbbeea7b061518 100644 GIT binary patch literal 7562 zcmeI1S6CBWx5pN)v$~ND&l#MG>R~MZwUE z^b!y$B7#B$3B5>fQbJA6fbaS4&ds?wH|KdKvom||tl4|5|Ex97FHuGYmpE7jSpfjx zxU8dj6#$^%S0*fCuiD|0erd&0H^?RDuAVm zB0&YbjA(EG00%(M0kCvX1RN9z4tT%;pY8zm=YYp(oFWjXC>N(_8HWer@PxRC^tgzI 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related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age. -\item \code{kin_summary}: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}: +\item \code{kin_full}: a data frame with year, cohort, focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), with living kin and death on that age. +\item \code{kin_summary}: a data frame with focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending arguments): } \itemize{ -\item \code{count}: count of living kin at actual age of focal +\item \code{count_living}: count of living kin at actual age of focal \item \code{mean_age}: mean age of each type of living kin. \item \code{sd_age}: standard deviation age of each type of living kin . \item \code{count_death}: count of death kin at specific age of focal. -\item \code{count_cum_death}: cumulated count of death kin at specific age of focal. +\item \code{count_cum_death}: cumulated count of death kin until specific age of focal. \item \code{mean_age_lost}: mean age where focal lost her relative. } } diff --git a/man/kin_multi_stage.Rd b/man/kin_multi_stage.Rd index e375b5f..519a4a5 100644 --- a/man/kin_multi_stage.Rd +++ b/man/kin_multi_stage.Rd @@ -29,8 +29,8 @@ kin_multi_stage( } \value{ A data frame with focal´s age, related ages and type of kin -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death, and specific stage. If \code{list_output = TRUE} then this is a list. +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), living and death kin counts, and specific stage. If \code{list_output = TRUE} then this is a list with elements as kin types. } \description{ -Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019). Stages are implied in length of input lists. +Implementation of age-stage kin estimates (multi-state) by Caswell (2020). Stages are implied in length of input lists. } diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd index 1951eb5..da16ad7 100644 --- a/man/kin_time_variant.Rd +++ b/man/kin_time_variant.Rd @@ -37,7 +37,7 @@ kin_time_variant( } \value{ A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives -(for example \code{d} is daughter, \code{oa} is older aunts, etc.), and age of (living or deceased) relatives. If \code{list_output = TRUE} then this is a list. +(for example \code{d} is daughter, \code{oa} is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If \code{list_output = TRUE} then this is a list. } \description{ Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021). diff --git a/man/plot_diagram.Rd b/man/plot_diagram.Rd index 509fc00..4068c74 100644 --- a/man/plot_diagram.Rd +++ b/man/plot_diagram.Rd @@ -9,7 +9,7 @@ plot_diagram(kin_total, rounding = 3) \arguments{ \item{rounding}{numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm.} -\item{kins_total}{vector. Colum names must be as \code{kins} output.} +\item{kins_total}{data.frame. kin type and some count in columns.} } \value{ A plot diff --git a/man/rename_kin.Rd b/man/rename_kin.Rd new file mode 100644 index 0000000..32b7ae4 --- /dev/null +++ b/man/rename_kin.Rd @@ -0,0 +1,11 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/aux_funs.R +\name{rename_kin} +\alias{rename_kin} +\title{rename kin} +\usage{ +rename_kin(df, consolidate = FALSE) +} +\description{ +Rename kin labels depending consolidate some types +} diff --git a/vignettes/Use.Rmd b/vignettes/Reference.Rmd similarity index 83% rename from vignettes/Use.Rmd rename to vignettes/Reference.Rmd index fa127a3..ccea4bb 100644 --- a/vignettes/Use.Rmd +++ b/vignettes/Reference.Rmd @@ -33,32 +33,20 @@ Let's visualize the distribution of relatives over focal's lifecourse: ```{r, fig.height=6, fig.width=8} swe_2015[["kin_summary"]] %>% ggplot() + - geom_line(aes(age_focal, count)) + + geom_line(aes(age_focal, count_living)) + theme_bw() + facet_wrap(~kin) ``` -Where each relative type is identified by a unique code: +Where each relative type is identified by a unique code. Note that the relationship codes used in `DemoKin` differ from those in Caswell (2019). The equivalence between the two set of codes is given in the following table: ```{r, fig.height=6, fig.width=8, echo=FALSE} -relatives <- c( - "coa" = "Cousins (through aunt older than mother)" - , "cya" = "Cousins (through aunt younger than mother)" - , "d" = "Daughter" - , "gd" = "Grand-daughter" - , "ggm" = "Great-grandmother" - , "gm" = "Grandmother" - , "m" = "Mother" - , "nos" = "Nieces through older sister" - , "nys" = "Nieces through younger sister" - , "oa" = "Aunt older than mother" - , "ya" = "Aunt younger than mother" - , "os" = "Older sister" - , "ys" = "Younger sister" - ) - -data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) %>% kable() +codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys") +caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n") +labels <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister") + +data.frame(DemoKin = codes, Caswell = caswell_codes, Label = labels, row.names = NULL) %>% kable(caption = "Equivalence between relative codes between DemoKin and Caswell (2019).") ``` @@ -66,9 +54,9 @@ We can also visualize the age distribution of relatives when focal is 35 for exa ```{r, fig.height=6, fig.width=8} swe_2015[["kin_full"]] %>% - filter(alive=="yes", age_focal == 35) %>% + filter(age_focal == 35) %>% ggplot() + - geom_line(aes(age_kin, count)) + + geom_line(aes(age_kin, living)) + geom_vline(xintercept = 35, color=2) + theme_bw() + facet_wrap(~kin) @@ -81,18 +69,18 @@ The `kin` function also includes a summary output with the count of living kin, ```{r, fig.height=6, fig.width=8} swe_2015[["kin_summary"]] %>% filter(age_focal == 35) %>% - select(kin, count, mean_age, sd_age) %>% + select(kin, count_living, mean_age, sd_age) %>% mutate_if(is.numeric, round, 2) %>% kable() ``` We can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35: -```{r} +```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} swe_2015_age35 <- swe_2015[["kin_summary"]] %>% filter(age_focal == 35) %>% - select(kin, count) -plot_diagram(swe_2015_age35) + select(kin, count = count_living) +plot_diagram(swe_2015_age35, rounding = 2) ``` @@ -107,8 +95,7 @@ swe_2015_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invaria output_kin = c("d","gd","ggd","m","gm","ggm")) swe_2015_time_varying$kin_full %>% - filter(alive=="yes") %>% - group_by(kin, cohort, age_focal) %>% summarise(count = sum(count)) %>% + group_by(kin, cohort, age_focal) %>% summarise(count = sum(living)) %>% ggplot(aes(age_focal,count,color=factor(cohort))) + scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+ geom_line(size=1,color=1)+ @@ -124,7 +111,7 @@ As you see the estimates stop at age 55, because no data is after 2015. Kin loss has studied consequences related to mental health, care support and household incomes over life course. Setting the parameter `alive="no"` we can see for a focal from 1900 cohort how many relatives loss during her life (output `kin_death_by_age_focal`): -```{r, fig.height=6, fig.width=8} +```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} swe_1900_time_varying <- kin(U = swe_px, f = swe_asfr, N = swe_pop, time_invariant = FALSE, output_cohort = 1900, output_kin = c("d","gd","ggd","m","gm","ggm")) @@ -150,20 +137,19 @@ swe_1900_time_varying$kin_summary %>% Once you have a full kin distribution by age, is possible to know what is the expected portion of living kin in some stage given a set of prevalence by age (could be a disease, employment, etc.). This kind of method is known as Sullivan in life table apporaches. A matrix formulation for same results can be found in Caswell (2019). This can be applied for time variant prevalence too. -```{r} -# let´s create some prevalence shcedule by age +```{r, message=FALSE, warning=FALSE} +# let´s create some prevalence by age swe_2015_prevalence <- tibble(age_kin = unique(swe_2015$kin_full$age_kin), prev = .005 * exp(.05 * age_kin)) -plot(swe_2015_prevalence) +# plot(swe_2015_prevalence) # join to kin count estimates and plot swe_2015$kin_full %>% - filter(alive=="yes") %>% left_join(swe_2015_prevalence) %>% group_by(kin, age_focal) %>% - summarise(prevalent = sum(count * prev), - no_prevalent = sum(count * (1-prev))) %>% + summarise(prevalent = sum(living * prev), + no_prevalent = sum(living * (1-prev))) %>% pivot_longer(cols = prevalent:no_prevalent, names_to = "prevalence_state", values_to = "count") %>% ggplot(aes(x=age_focal, y = count)) + geom_area(aes(fill=prevalence_state)) + @@ -190,14 +176,14 @@ demokin_svk1980_caswell2020 <- kin_multi_stage(U = svk_Uxs, We can see age-parity distribution of aunts, when focal is 20 or 60 (similar to figure 4 in Caswell(2021)). -```{r} +```{r, message=FALSE, warning=FALSE} demokin_svk1980_caswell2020 %>% - filter(kin %in% c("oa","ya"), alive == "yes", age_focal %in% c(20,60)) %>% + filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% mutate(parity = as.integer(stage_kin)-1, parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), parity = fct_rev(parity)) %>% group_by(age_focal, age_kin, parity) %>% - summarise(count= sum(count)) %>% + summarise(count= sum(living)) %>% ggplot() + geom_bar(aes(x=age_kin, y = count, fill=parity), stat = "identity") + geom_vline(aes(xintercept = age_focal), col=2) + @@ -207,14 +193,14 @@ demokin_svk1980_caswell2020 %>% Also we can see, When focal grows up, the portion of living daughters and mothers in different parity stages (similar to figure 9 and 10 in Caswell(2021)). -```{r} +```{r, message=FALSE, warning=FALSE} demokin_svk1980_caswell2020 %>% - filter(kin %in% c("d","m"), alive == "yes") %>% + filter(kin %in% c("d","m")) %>% mutate(parity = as.integer(stage_kin)-1, parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)), parity = fct_rev(parity)) %>% group_by(age_focal, kin, parity) %>% - summarise(count= sum(count)) %>% + summarise(count= sum(living)) %>% ggplot() + geom_bar(aes(x=age_focal, y = count, fill=parity), stat = "identity") + theme_bw() + From 0248f30e79cab6a866182eb81dcddaf9d11ac65e Mon Sep 17 00:00:00 2001 From: alburezg Date: Tue, 30 Aug 2022 16:35:06 +0200 Subject: [PATCH 08/21] improve plot quality --- README.Rmd | 2 +- README.md | 10 +++++----- man/figures/README-unnamed-chunk-5-1.png | Bin 7562 -> 131389 bytes 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.Rmd b/README.Rmd index 800e3da..f36d3c5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -73,7 +73,7 @@ data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) %>% We can visualize the implied kin counts for a Focal aged 35 in Sweden (with freeze rates from 2015) using a network or 'Keyfitz' kinship diagram using the `plot_diagram` function: -```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE} +```{r, fig.height=6, dpi=900,fig.width=8, message=FALSE, warning=FALSE} plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% dplyr::filter(age_focal == 35) %>% dplyr::select(kin, count = count_living), diff --git a/README.md b/README.md index 9ec3158..691d92f 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ Relatives for the `selected_kin` argument of function `kin` are identified by a unique code. | Code | Relative | -|:-----|:-------------------------------------------| +| :--- | :----------------------------------------- | | coa | Cousins (through aunt older than mother) | | cya | Cousins (through aunt younger than mother) | | d | Daughter | @@ -77,8 +77,8 @@ plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% For more details, including an extension to time varying populations rates, relative´s death distribution and multi-state model, see `vignette("Use")`. Note that if the vignette does not load, you may need -to install the package as -`devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. +to install the package as `devtools::install_github("IvanWilli/DemoKin", +build_vignettes = T)`. ## Citation @@ -107,8 +107,8 @@ Population Biology 5(1):1–27. . 3. Improve kinship diagram visualization 4. Improve documentation and vignette of package -## Get involved! +## Get involved\! `DemoKin` is giving its first steps. If you’re interested in contributing, please get in touch, create an issue, or submit a pull -request. We look forward to hearing from you! +request. 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z_%327=PPY!l9cn-QSk>@~0R4DCbCyB|<$U0%^PqyoS5A$(IY zO3%~!>raGfz=Zh6i12FAFYS1TX&;}MS*f4&f={BdVC#3_$pbe(``Vv+)HvTC_)t(P zxk(i*lebn%*DOy=rrynHWB7Qm1LJ6Wlvs85CN Qa8n7kxq7>lZ>1#t2dc%*;s5{u literal 0 HcmV?d00001 diff --git a/README.Rmd b/README.Rmd index f36d3c5..e0c92c3 100644 --- a/README.Rmd +++ b/README.Rmd @@ -12,14 +12,19 @@ knitr::opts_chunk$set( out.width = "100%" ) devtools::load_all() -library(tidyverse) -library(knitr) library(DemoKin) +library(knitr) +``` + + +```{r, out.width = "250px", echo=F} +knitr::include_graphics("DemoKin-Logo.png") ``` -# DemoKin +# DemoKin: Implementing matrix kinship models in R +`r format(Sys.time(), "%b %d %Y")` -`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019) and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974). +`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019), Caswell (2020), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974). ## Installation @@ -30,25 +35,38 @@ You can install the development version from GitHub with: devtools::install_github("IvanWilli/DemoKin") ``` -## Example -Consider an average Swedish woman in year 2015 (i.e., 'Focal'). We assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life. +## Time-invariant example + +Consider an average Swedish woman called 'Focal'. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life (the 'time-invariant' assumption in Caswell [2019]). We then ask: -> How can we characterize the kinship network of an average member of the population ('Focal')? + +> How many living relatives does Focal have at each age? -For this exercise, we'll use the Swedish data pre-loaded with `DemoKin`. +Let's explore this Using the Swedish data included with `DemoKin`. ```{r, fig.height=6, fig.width=8} library(DemoKin) -swe_surv_2015 <- swe_px[,"2015"] -swe_asfr_2015 <- swe_asfr[,"2015"] +swe_surv_2015 <- DemoKin::swe_px[,"2015"] +swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` *px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). -Relatives for the `selected_kin` argument of function `kin` are identified by a unique code. +Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`: + +```{r, fig.height=6, dpi=900,fig.width=8, message=FALSE, warning=FALSE} +# We need to reformat the data a little bit +kin_total <- swe_2015[["kin_summary"]] +# Keep only data for Focal's age 35 +kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")] +names(kin_total) <- c("kin", "count") +plot_diagram(kin_total, rounding = 2) +``` + +Relatives are identified by a unique code: ```{r, fig.height=6, fig.width=8, echo=FALSE} @@ -60,50 +78,45 @@ relatives <- c( , "ggm" = "Great-grandmother" , "gm" = "Grandmother" , "m" = "Mother" - , "nos" = "Nieces through older sister" - , "nys" = "Nieces through younger sister" + , "nos" = "Nieces through mother's older sister" + , "nys" = "Nieces through mother's younger sister" , "oa" = "Aunt older than mother" , "ya" = "Aunt younger than mother" , "os" = "Older sister" , "ys" = "Younger sister" ) -data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) %>% kable() +kable(data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) ) ``` -We can visualize the implied kin counts for a Focal aged 35 in Sweden (with freeze rates from 2015) using a network or 'Keyfitz' kinship diagram using the `plot_diagram` function: - -```{r, fig.height=6, dpi=900,fig.width=8, message=FALSE, warning=FALSE} -plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% - dplyr::filter(age_focal == 35) %>% - dplyr::select(kin, count = count_living), - rounding = 2) -``` +## Vignette -For more details, including an extension to time varying populations rates, relative´s death distribution and multi-state model, see `vignette("Use")`. -Note that if the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. +For more details, including an extension to time varying-populations rates, deceased kin, and multi-state models, see `vignette("Reference", package = "DemoKin")`. +If the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation -Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to estimate kinship networks in stable and non-stable populations. URL: https://github.com/IvanWilli/DemoKin. +Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: https://github.com/IvanWilli/DemoKin. + +## Get involved! + +`DemoKin` is under constant development. +If you're interested in contributing, please get in touch, create an issue, or submit a pull request. +We look forward to hearing from you! ## References -Caswell, H. (2019). The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24. +Caswell, H. 2019. The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24. + +Caswell, H. 2020. The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research 42: 1097-1144. doi:10.4054/DemRes.2020.42.38. Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46. doi:10.4054/DemRes.2021.45.16. Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. doi:10.1016/0040-5809(74)90049-5. -## Next steps: + + + + -1. Implement two-sex matrix kinship models -1. Create a hex logo for package -1. Improve kinship diagram visualization -1. Improve documentation and vignette of package -## Get involved! - -`DemoKin` is giving its first steps. -If you're interested in contributing, please get in touch, create an issue, or submit a pull request. -We look forward to hearing from you! diff --git a/README.md b/README.md index 691d92f..947d446 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,17 @@ -# DemoKin + + +# DemoKin: Implementing matrix kinship models in R + +Aug 31 2022 `DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell -(2019) and Caswell and Song (2021). It draws on previous theoretical -development by Goodman, Keyfitz and Pullum (1974). +(2019), Caswell (2020), and Caswell and Song (2021). It draws on +previous theoretical development by Goodman, Keyfitz and Pullum (1974). ## Installation @@ -18,32 +22,47 @@ You can install the development version from GitHub with: devtools::install_github("IvanWilli/DemoKin") ``` -## Example +## Time-invariant example -Consider an average Swedish woman in year 2015 (i.e., ‘Focal’). We +Consider an average Swedish woman called ‘Focal’. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their -life. +life (the ‘time-invariant’ assumption in Caswell \[2019\]). We then ask: -> How can we characterize the kinship network of an average member of -> the population (‘Focal’)? + + +> How many living relatives does Focal have at each age? -For this exercise, we’ll use the Swedish data pre-loaded with `DemoKin`. +Let’s explore this Using the Swedish data included with `DemoKin`. ``` r library(DemoKin) -swe_surv_2015 <- swe_px[,"2015"] -swe_asfr_2015 <- swe_asfr[,"2015"] +swe_surv_2015 <- DemoKin::swe_px[,"2015"] +swe_asfr_2015 <- DemoKin::swe_asfr[,"2015"] swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) ``` *px* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). -Relatives for the `selected_kin` argument of function `kin` are -identified by a unique code. +Now, we can visualize the implied kin counts (i.e., the average number +of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship +diagram with the function `plot_diagram`: + +``` r +# We need to reformat the data a little bit +kin_total <- swe_2015[["kin_summary"]] +# Keep only data for Focal's age 35 +kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")] +names(kin_total) <- c("kin", "count") +plot_diagram(kin_total, rounding = 2) +``` + + + +Relatives are identified by a unique code: | Code | Relative | | :--- | :----------------------------------------- | @@ -54,44 +73,43 @@ identified by a unique code. | ggm | Great-grandmother | | gm | Grandmother | | m | Mother | -| nos | Nieces through older sister | -| nys | Nieces through younger sister | +| nos | Nieces through mother’s older sister | +| nys | Nieces through mother’s younger sister | | oa | Aunt older than mother | | ya | Aunt younger than mother | | os | Older sister | | ys | Younger sister | -We can visualize the implied kin counts for a Focal aged 35 in Sweden -(with freeze rates from 2015) using a network or ‘Keyfitz’ kinship -diagram using the `plot_diagram` function: - -``` r -plot_diagram(kin_total = swe_2015[["kin_summary"]] %>% - dplyr::filter(age_focal == 35) %>% - dplyr::select(kin, count = count_living), - rounding = 2) -``` - - +## Vignette -For more details, including an extension to time varying populations -rates, relative´s death distribution and multi-state model, see -`vignette("Use")`. Note that if the vignette does not load, you may need -to install the package as `devtools::install_github("IvanWilli/DemoKin", +For more details, including an extension to time varying-populations +rates, deceased kin, and multi-state models, see `vignette("Reference", +package = "DemoKin")`. If the vignette does not load, you may need to +install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`. ## Citation Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. -(2021) DemoKin: An R package to estimate kinship networks in stable and -non-stable populations. URL: . +(2021) DemoKin: An R package to implement demographic matrix kinship +models. URL: . + +## Get involved\! + +`DemoKin` is under constant development. If you’re interested in +contributing, please get in touch, create an issue, or submit a pull +request. We look forward to hearing from you\! ## References -Caswell, H. (2019). The formal demography of kinship: A matrix +Caswell, H. 2019. The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712. . +Caswell, H. 2020. The formal demography of kinship II: Multistate +models, parity, and sibship. Demographic Research 42: 1097-1144. +. + Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46. . @@ -100,15 +118,8 @@ Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. . -## Next steps: - -1. Implement two-sex matrix kinship models -2. Create a hex logo for package -3. Improve kinship diagram visualization -4. Improve documentation and vignette of package + -## Get involved\! + -`DemoKin` is giving its first steps. If you’re interested in -contributing, please get in touch, create an issue, or submit a pull -request. We look forward to hearing from you\! + diff --git a/issues.txt b/issues.txt new file mode 100644 index 0000000..b5b44e9 --- /dev/null +++ b/issues.txt @@ -0,0 +1 @@ +- remove library(igraph) from plot_diagram.R \ No newline at end of file diff --git a/man/figures/README-unnamed-chunk-3-1.png b/man/figures/README-unnamed-chunk-3-1.png deleted file mode 100644 index dd1e74f470b9cb99dfbf1f465cbc9917b0fd18b2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 28630 zcmeFZcTiL9|1BCsQBlAO2m&Ht0Rg3mfD}b~lU@T@sG;{>L{O<3n$oLukQN|R3(`A; znjq3b4JC8}_u0JP_nzNv=bV4;%)K)wGY$yJ-p_var>xIfYxCi`iYyf+10@Uwqmq|< zrVfLV<6*E9&(2VQp9E1-H-Rr_?d9~GV6aQh&_6Pq8H+m%b^|8=?1`p(;u6-&U30T; zo763NgY}aW^?ms>R}ILClhAN?$~I4eT%kIZ+ean@v0_h+s^QMDvI)|*_8!##iBbM&t+tqeS%6Qi|h7X)(^_XCbk|G zv%*YF&heu`Xhfrwx^48+GsP#=f?R8Puoan=l?jDE7M2tZE1!y@y0z3?(s*3XrM+ml zUSX-fHGYeGjTEY`mh)!R)U(L*1Co<6W-TKeU2lBILk8>F$Aef^%1ys7b*-?_ia^*G zvT}HlMkxl@oR~9UF<9z=C#nF~LxEgPgpc{+=VwA01|idNS!a|WWr 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z!JOT+o=6FA)NNq|FTc`}6y6n|k4Es=I8k_LUE0E`7@F*LD8j9XbSkTe%#y>qx?s~+ zCN!O|ebP9ft&hIcmO#FN)Frk#LMt7E&t130cwuhly&T|ob;?&_O@;YuhFQ(JcK)`# z{-Fb0&3vBOnY_9DJ`YqE)nzLM-5+g$>t?sG&$0VbYlsBTYw#W@oZgn0z|h*_5l0Tw z+~@i>c)14DoU)6Nd*B;bY{Rg#7>q`!#}xEN+3^NU=MVUfHqxVhStZ3RFaJ>h#zr3Z zP-s&?EyEuSb Date: Wed, 31 Aug 2022 12:10:32 +0200 Subject: [PATCH 10/21] edited readme --- README.Rmd | 24 +-- README.md | 35 ++-- issues.txt | 1 - man/figures/README-unnamed-chunk-5-1.png | Bin 131389 -> 199981 bytes vignettes/Reference.Rmd | 221 +++++++++++++++-------- 5 files changed, 167 insertions(+), 114 deletions(-) delete mode 100644 issues.txt diff --git a/README.Rmd b/README.Rmd index e0c92c3..651c82b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -17,11 +17,11 @@ library(knitr) ``` -```{r, out.width = "250px", echo=F} +```{r, out.width = "250px", fig.align = 'right', echo=F} knitr::include_graphics("DemoKin-Logo.png") ``` -# DemoKin: Implementing matrix kinship models in R +# DemoKin: Matrix kinship models in R `r format(Sys.time(), "%b %d %Y")` `DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019), Caswell (2020), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974). @@ -57,7 +57,7 @@ swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE) Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`: -```{r, fig.height=6, dpi=900,fig.width=8, message=FALSE, warning=FALSE} +```{r, fig.height=6, dpi=1200,fig.width=8, message=FALSE, warning=FALSE} # We need to reformat the data a little bit kin_total <- swe_2015[["kin_summary"]] # Keep only data for Focal's age 35 @@ -70,23 +70,7 @@ Relatives are identified by a unique code: ```{r, fig.height=6, fig.width=8, echo=FALSE} -relatives <- c( - "coa" = "Cousins (through aunt older than mother)" - , "cya" = "Cousins (through aunt younger than mother)" - , "d" = "Daughter" - , "gd" = "Grand-daughter" - , "ggm" = "Great-grandmother" - , "gm" = "Grandmother" - , "m" = "Mother" - , "nos" = "Nieces through mother's older sister" - , "nys" = "Nieces through mother's younger sister" - , "oa" = "Aunt older than mother" - , "ya" = "Aunt younger than mother" - , "os" = "Older sister" - , "ys" = "Younger sister" - ) - -kable(data.frame(Code = names(relatives), Relative = relatives, row.names = NULL) ) +kable(DemoKin::demokin_codes()[-2]) ``` ## Vignette diff --git a/README.md b/README.md index 947d446..c72bc32 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ - + -# DemoKin: Implementing matrix kinship models in R +# DemoKin: Matrix kinship models in R Aug 31 2022 @@ -64,21 +64,22 @@ plot_diagram(kin_total, rounding = 2) Relatives are identified by a unique code: -| Code | Relative | -| :--- | :----------------------------------------- | -| coa | Cousins (through aunt older than mother) | -| cya | Cousins (through aunt younger than mother) | -| d | Daughter | -| gd | Grand-daughter | -| ggm | Great-grandmother | -| gm | Grandmother | -| m | Mother | -| nos | Nieces through mother’s older sister | -| nys | Nieces through mother’s younger sister | -| oa | Aunt older than mother | -| ya | Aunt younger than mother | -| os | Older sister | -| ys | Younger sister | +| DemoKin | Label | +| :------ | :------------------------- | +| coa | Cousins from older aunt | +| cya | Cousins from younger aunt | +| d | Daughter | +| gd | Grand-daughter | +| ggd | Great-grand-daughter | +| ggm | Great-grandmother | +| gm | Grandmother | +| m | Mother | +| nos | Nieces from older sister | +| nys | Nieces from younger sister | +| oa | Aunt older than mother | +| ya | Aunt younger than mother | +| os | Older sister | +| ys | Younger sister | ## Vignette diff --git a/issues.txt b/issues.txt deleted file mode 100644 index b5b44e9..0000000 --- a/issues.txt +++ /dev/null @@ -1 +0,0 @@ -- remove library(igraph) from plot_diagram.R \ No newline at end of file diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png index 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