move prior weights to model arguments

DESCRIPTION

@@ -2,7 +2,7 @@ Type: Package
 Package: EpiNow2
 Title: Estimate Real-Time Case Counts and Time-Varying
     Epidemiological Parameters
-Version: 1.3.6.9007
+Version: 1.3.6.9008
 Authors@R: 
     c(person(given = "Sam",
              family = "Abbott",

NEWS.md

@@ -29,6 +29,7 @@ This release is in development. For a stable release install 1.3.5 from CRAN.
 * Added content to the vignette for the estimate_truncation model. By @sbfnk in #439 and reviewed by @seabbs.
 * Added a feature to the `estimate_truncation` to allow it to be applied to time series that are shorter than the truncation max. By @sbfnk in #438 and reviewed by @seabbs.
 * Changed the `estimate_truncation` to use the `dist_spec` interface, deprecating existing options `max_trunc` and `trunc_dist`. By @sbfnk in #448 reviewed by @seabbs.
+* Added a `weigh_delay_priors` argument to the main functions, allowing the users to choose whether to weigh delay priors by the number of data points or not. By @sbfnk in #450 and reviewed by @seabbs.
 
 ## Documentation
 

R/create.R

@@ -640,12 +640,11 @@ create_stan_args <- function(stan = stan_opts(),
 ##'
 ##' @param ... Named delay distributions specified using `dist_spec()`.
 ##' The names are assigned to IDs
-##' @param ot Integer, number of observations (needed if weighing any priors)
-##' with the number of observations
+##' @param weight Numeric, weight associated with delay priors; default: 1
 ##' @return A list of variables as expected by the stan model
 ##' @importFrom purrr transpose map
 ##' @author Sebastian Funk
-create_stan_delays <- function(..., ot) {
+create_stan_delays <- function(..., weight = 1) {
   dot_args <- list(...)
   ## combine delays
   combined_delays <- unclass(c(...))
@@ -673,9 +672,7 @@ create_stan_delays <- function(..., ot) {
   ## map pmfs
   ret$np_pmf_groups <- array(c(0, cumsum(combined_delays$np_pmf_length)) + 1)
   ## assign prior weights
-  if (any(ret$weight == 0)) {
-    ret$weight[ret$weight == 0] <- ot
-  }
+  ret$weight <- array(rep(weight, ret$n_p))
   ## remove auxiliary variables
   ret$fixed <- NULL
   ret$np_pmf_length <- NULL

R/dist.R

@@ -889,11 +889,6 @@ tune_inv_gamma <- function(lower = 2, upper = 21) {
 #' as coming from fixed (vs uncertain) distributions. Overrides any values
 #' assigned to \code{mean_sd} and \code{sd_sd} by setting them to zero.
 #' reduces compute requirement but may produce spuriously precise estimates.
-#' @param prior_weight Integer, weight given to the generation time prior.
-#' By default (prior_weight = 0) the priors will be weighted by the number of
-#' observation data points, usually preventing the posteriors from shifting
-#' much from the given distribution. Another sensible option would be 1,
-#' i.e. treating the generation time distribution as a single parameter.
 #' @return A list of distribution options.
 #'
 #' @author Sebastian Funk
@@ -910,7 +905,7 @@ tune_inv_gamma <- function(lower = 2, upper = 21) {
 #' )
 dist_spec <- function(mean, sd = 0, mean_sd = 0, sd_sd = 0,
                       distribution = c("lognormal", "gamma"), max,
-                      pmf = numeric(0), fixed = FALSE, prior_weight = 0L) {
+                      pmf = numeric(0), fixed = FALSE) {
   ## check if parametric or nonparametric
   if (length(pmf) > 0 &&
     !all(
@@ -978,7 +973,6 @@ dist_spec <- function(mean, sd = 0, mean_sd = 0, sd_sd = 0,
           np_pmf_max = 0,
           np_pmf = numeric(0),
           np_pmf_length = integer(0),
-          weight = numeric(0),
           fixed = integer(0)
         ))
       } else { ## parametric fixed
@@ -1017,7 +1011,6 @@ dist_spec <- function(mean, sd = 0, mean_sd = 0, sd_sd = 0,
         np_pmf_max = length(pmf),
         np_pmf = pmf,
         np_pmf_length = length(pmf),
-        weight = numeric(0),
         fixed = 1L
       ))
     }
@@ -1036,7 +1029,6 @@ dist_spec <- function(mean, sd = 0, mean_sd = 0, sd_sd = 0,
       np_pmf_max = 0,
       np_pmf = numeric(0),
       np_pmf_length = integer(0),
-      weight = prior_weight,
       fixed = 0L
     )
   }

R/estimate_infections.R

@@ -35,6 +35,12 @@
 #' @param horizon Numeric, defaults to 7. Number of days into the future to
 #' forecast.
 #'
+#' @param weigh_delay_priors Logical. If TRUE (default), all delay distribution
+#' priors will be weighted by the number of observation data points, in doing so
+#' approximately placing an independent prior at each time step and usually
+#' preventing the posteriors from shifting. If FALSE, no weight will be applied,
+#' i.e. delay distributions will be treated as a single parameters.
+#'
 #' @param verbose Logical, defaults to `TRUE` when used interactively and
 #' otherwise `FALSE`. Should verbose debug progress messages be printed.
 #' Corresponds to the "DEBUG" level from `futile.logger`. See `setup_logging`
@@ -48,6 +54,7 @@
 #' @seealso epinow regional_epinow forecast_infections simulate_infections
 #' @inheritParams create_stan_args
 #' @inheritParams create_stan_data
+#' @inheritParams create_stan_data
 #' @inheritParams create_gp_data
 #' @inheritParams fit_model_with_nuts
 #' @inheritParams create_clean_reported_cases
@@ -245,6 +252,7 @@ estimate_infections <- function(reported_cases,
                                 CrIs = c(0.2, 0.5, 0.9),
                                 filter_leading_zeros = TRUE,
                                 zero_threshold = Inf,
+                                weigh_delay_priors = TRUE,
                                 id = "estimate_infections",
                                 verbose = interactive()) {
   set_dt_single_thread()
@@ -311,7 +319,9 @@ estimate_infections <- function(reported_cases,
     gt = generation_time,
     delay = delays,
     trunc = truncation,
-    ot = data$t - data$seeding_time - data$horizon
+    weight = ifelse(
+      weigh_delay_priors, data$t - data$seeding_time - data$horizon, 1
+    )
   ))
 
   # Set up default settings

R/estimate_secondary.R

@@ -40,6 +40,13 @@
 #' use for estimation but not to fit to at the beginning of the time series.
 #' This must be less than the number of observations.
 #'
+#' @param weigh_delay_priors Logical. If TRUE, all delay distribution priors
+#' will be weighted by the number of observation data points, in doing so
+#' approximately placing an independent prior at each time step and usually
+#' preventing the posteriors from shifting. If FALSE (default), no weight will
+#' be applied, i.e. delay distributions will be treated as a single
+#' parameters.
+#'
 #' @param verbose Logical, should model fitting progress be returned. Defaults
 #' to `interactive()`.
 #'
@@ -129,8 +136,7 @@ estimate_secondary <- function(reports,
                                delays = delay_opts(
                                  dist_spec(
                                    mean = 2.5, mean_sd = 0.5,
-                                   sd = 0.47, sd_sd = 0.25, max = 30,
-                                   prior_weight = 1
+                                   sd = 0.47, sd_sd = 0.25, max = 30
                                  )
                                ),
                                truncation = trunc_opts(),
@@ -139,6 +145,7 @@ estimate_secondary <- function(reports,
                                CrIs = c(0.2, 0.5, 0.9),
                                priors = NULL,
                                model = NULL,
+                               weigh_delay_priors = FALSE,
                                verbose = interactive(),
                                ...) {
   reports <- data.table::as.data.table(reports)
@@ -161,7 +168,7 @@ estimate_secondary <- function(reports,
   data <- c(data, create_stan_delays(
     delay = delays,
     trunc = truncation,
-    ot = data$t
+    weight = ifelse(weigh_delay_priors, data$t, 1)
   ))
 
   # observation model data

R/estimate_truncation.R

@@ -47,6 +47,13 @@
 #' @param model A compiled stan model to override the default model. May be
 #' useful for package developers or those developing extensions.
 #'
+#' @param weigh_delay_priors Logical. If TRUE, all delay distribution priors
+#' will be weighted by the number of observation data points, in doing so
+#' approximately placing an independent prior at each time step and usually
+#' preventing the posteriors from shifting. If FALSE (default), no weight will
+#' be applied, i.e. delay distributions will be treated as a single
+#' parameters.
+#'
 #' @param verbose Logical, should model fitting progress be returned.
 #'
 #' @param ... Additional parameters to pass to `rstan::sampling`.
@@ -134,10 +141,11 @@ estimate_truncation <- function(obs, max_truncation, trunc_max = 10,
                                 trunc_dist = "lognormal",
                                 truncation = dist_spec(
                                   mean = 0, sd = 0, mean_sd = 1, sd_sd = 1,
-                                  max = 10, prior_weight = 1L
+                                  max = 10
                                 ),
                                 model = NULL,
                                 CrIs = c(0.2, 0.5, 0.9),
+                                weigh_delay_priors = FALSE,
                                 verbose = TRUE,
                                 ...) {
 
@@ -189,7 +197,7 @@ estimate_truncation <- function(obs, max_truncation, trunc_max = 10,
   if (construct_trunc) {
     truncation <- dist_spec(
       mean = 0, mean_sd = 1, sd = 0, sd_sd = 1, distribution = trunc_dist,
-      max = trunc_max, prior_weight = 1
+      max = trunc_max
     )
   }
 
@@ -216,9 +224,10 @@ estimate_truncation <- function(obs, max_truncation, trunc_max = 10,
     obs_sets = ncol(obs_data)
   )
 
-  data <- c(data,
-    create_stan_delays(trunc = truncation)
-  )
+  data <- c(data, create_stan_delays(
+    trunc = truncation,
+    weight = ifelse(weigh_delay_priors, data$t, 1)
+  ))
 
   ## convert to integer
   data$trunc_dist <-

tests/testthat/test-delays.R

@@ -6,7 +6,7 @@ test_stan_delays <- function(generation_time = generation_time_opts(),
     generation_time = generation_time,
     delays = delays,
     truncation = truncation,
-    ot = 10
+    weight = 10
   )
   return(unlist(unname(data[params])))
 }

tests/testthat/test-estimate_secondary.R

@@ -101,14 +101,14 @@ test_that("forecast_secondary can return values from simulated data and plot
   expect_error(plot(inc_preds, new_obs = cases, from = "2020-05-01"), NA)
 })
 
-test_that("estimate_secondary works with prior_weight = 0", {
-  weight_0_delays <- dist_spec(
+test_that("estimate_secondary works with weigh_delay_priors = TRUE", {
+  delays <- dist_spec(
     mean = 2.5, mean_sd = 0.5, sd = 0.47, sd_sd = 0.25, max = 30
   )
-  inc_weight_0 <- estimate_secondary(
-    cases[1:60], delays = weight_0_delays,
+  inc_weigh <- estimate_secondary(
+    cases[1:60], delays = delays,
     obs = obs_opts(scale = list(mean = 0.2, sd = 0.2), week_effect = FALSE),
-    verbose = FALSE
+    weigh_delay_priors = TRUE, verbose = FALSE
   )
-  expect_s3_class(inc_weight_0, "estimate_secondary")
+  expect_s3_class(inc_weigh, "estimate_secondary")
 })

tests/testthat/test-estimate_truncation.R

@@ -15,8 +15,7 @@ trunc_dist <- dist_spec(
   mean_sd = 0.1,
   sd = convert_to_logsd(3, 2),
   sd_sd = 0.1,
-  max = 10,
-  prior_weight = 1
+  max = 10
 )
 
 # apply truncation to example data