Get parameters

NAMESPACE

@@ -66,8 +66,11 @@ export(forecast_secondary)
 export(gamma_dist_def)
 export(generation_time_opts)
 export(get_dist)
+export(get_distribution)
 export(get_generation_time)
 export(get_incubation_period)
+export(get_parameters)
+export(get_pmf)
 export(get_raw_result)
 export(get_regional_results)
 export(get_regions)
@@ -77,6 +80,7 @@ export(growth_to_R)
 export(lognorm_dist_def)
 export(make_conf)
 export(map_prob_change)
+export(new_dist_spec)
 export(obs_opts)
 export(opts_list)
 export(plot_estimates)

NEWS.md

@@ -14,6 +14,7 @@
 * A new `simulate_infections` function has been added that can be used to simulate from the model from given initial conditions and parameters. By @sbfnk in #557 and reviewed by @jamesmbaazam.
 * The function `init_cumulative_fit()` has been deprecated. By @jamesmbaazam in #541 and reviewed by @sbfnk.
 * The interface to generating delay distributions has been completely overhauled. Instead of calling `dist_spec()` users now specify distributions using functions that represent the available distributions, i.e. `LogNormal()`, `Gamma()` and `Fixed()`. Uncertainty is specified using calls of the same nature, to `Normal()`. More information on the underlying design can be found in `inst/dev/design_dist.md` By @sbfnk in #504 and reviewed by @seabbs.
+* The accessor functions `get_parameters()`, `get_pmf()`, and `get_distribution()` have been added to extract elements of a <dist_spec> object. By @sbfnk in #646 and reviewed by @jamesmbaazam.
 * The functions `sample_approx_dist()`, `report_cases()`, and `adjust_infection_reports()` have been deprecated as the functionality they provide can now be achieved with `simulate_secondary()`. See #597 by @jamesmbaazam and reviewed by @sbfnk.
 
 ## Documentation

R/create.R

@@ -82,9 +82,7 @@ create_clean_reported_cases <- function(data, horizon = 0,
 #' @description `r lifecycle::badge("stable")`
 #' Creates a complete data set without NA values and appropriate indices
 #'
-#' @param cases; data frame with a column "confirm" that may contain NA values
-#' @param burn_in; integer (default 0). Number of days to remove from the
-#' start of the time series be filtered out.
+#' @param cases data frame with a column "confirm" that may contain NA values
 #'
 #' @return A data frame without NA values, with two columns: confirm (number)
 #' @importFrom data.table setDT

R/deprecated.R

@@ -273,8 +273,8 @@ dist_spec <- function(distribution = c(
         mean = Normal(mean, mean_sd),
         sd = Normal(sd, sd_sd)
       )
-      params_mean <- vapply(temp_dist[[1]]$parameters, mean, numeric(1))
-      params_sd <- vapply(temp_dist[[1]]$parameters, sd_dist, numeric(1))
+      params_mean <- vapply(get_parameters(temp_dist), mean, numeric(1))
+      params_sd <- vapply(get_parameters(temp_dist), sd_dist, numeric(1))
     } else if (distribution == "normal") {
       params_mean <- c(mean = mean, sd = sd)
       params_sd <- c(mean = mean_sd, sd = sd_sd)

R/dist_spec.R

@@ -582,10 +582,10 @@ print.dist_spec <- function(x, ...) {
     } else if (x[[i]]$distribution == "fixed") {
       ## fixed
       cat(indent_str, "- fixed value:\n", sep = "")
-      if (is.numeric(x[[i]]$parameters$value)) {
-        cat(indent_str, "  ", x[[i]]$parameters$value, "\n", sep = "")
+      if (is.numeric(get_parameters(x, i)$value)) {
+        cat(indent_str, "  ", get_parameters(x, i)$value, "\n", sep = "")
       } else {
-        .print.dist_spec(x[[i]]$parameters$value, indent = indent + 4)
+        .print.dist_spec(get_parameters(x, i)$value, indent = indent + 4)
       }
     } else {
       ## parametric
@@ -595,18 +595,18 @@ print.dist_spec <- function(x, ...) {
       }
       cat(":\n")
       ## loop over natural parameters and print
-      for (param in names(x[[i]]$parameters)) {
+      for (param in names(get_parameters(x, i))) {
         cat(
           indent_str, "  ", param, ":\n", sep = ""
         )
-        if (is.numeric(x[[i]]$parameters[[param]])) {
+        if (is.numeric(get_parameters(x, i)[[param]])) {
           cat(
             indent_str, "    ",
-            signif(x[[i]]$parameters[[param]], digits = 2), "\n",
+            signif(get_parameters(x, i)[[param]], digits = 2), "\n",
             sep = ""
           )
         } else {
-          .print.dist_spec(x[[i]]$parameters[[param]], indent = indent + 4)
+          .print.dist_spec(get_parameters(x, i)[[param]], indent = indent + 4)
         }
       }
     }
@@ -654,12 +654,12 @@ plot.dist_spec <- function(x, ...) {
   for (i in seq_along(x)) {
     if (x[[i]]$distribution == "nonparametric") {
       # Fixed distribution
-      pmf <- x[[i]]$pmf
+      pmf <- get_pmf(x, i)
       dist_name <- paste0("Nonparametric", " (ID: ", i, ")")
     } else {
       # Uncertain distribution
       c_dist <- discretise(fix_dist(extract_single_dist(x, i)))
-      pmf <- c_dist[[1]]$pmf
+      pmf <- get_pmf(c_dist)
       dist_name <- paste0(
         ifelse(is.na(dist_sd[i]), "Uncertain ", ""),
         x[[i]]$distribution, " (ID: ", i, ")"
@@ -951,14 +951,12 @@ extract_params <- function(params, distribution) {
 #' @inheritParams extract_params
 #' @importFrom purrr walk
 #' @return A `dist_spec` of the given specification.
-#' @keywords internal
+#' @export
 #' @examples
-#' \dontrun{
 #' new_dist_spec(
 #'   params = list(mean = 2, sd = 1, max = Inf),
 #'   distribution = "normal"
 #' )
-#' }
 new_dist_spec <- function(params, distribution) {
   if (distribution == "nonparametric") {
     ## nonparametric distribution
@@ -968,8 +966,12 @@ new_dist_spec <- function(params, distribution) {
     )
   } else {
     ## process min/max first
-    max <- params$max
-    params$max <- NULL
+    if (is.null(params$max)) {
+      max <- Inf
+    } else {
+      max <- params$max
+      params$max <- NULL
+    }
     ## extract parameters and convert all to dist_spec
     params <- extract_params(params, distribution)
     ## fixed distribution
@@ -1100,3 +1102,86 @@ convert_to_natural <- function(params, distribution) {
   }
   return(params)
 }
+
+##' Perform checks for `<dist_spec>` `get_...` functions
+##'
+##' @param x A `<dist_spec>`.
+##' @param id Integer; the id of the distribution to get parameters of (if x is
+##' a composite distribution). If `x` is a single distribution this is ignored
+##' and can be left as `NULL`.
+##' @return The id to use.
+##' @keywords internal
+##' @author Sebastian Funk
+get_dist_spec_id <- function(x, id) {
+  if (!is.null(id) && id > length(x)) {
+    stop(
+      "`id` can't be greater than the number of distributions (", length(x),
+      ")."
+    )
+  }
+  if (length(x) > 1) {
+    if (is.null(id)) {
+      stop("`id` must be specified when `x` is a composite distribution.")
+    }
+  } else {
+    id <- 1
+  }
+  return(id)
+}
+
+##' Get parameters of a parametric distribution
+##'
+##' @inheritParams get_dist_spec_id
+##' @description `r lifecycle::badge("experimental")`
+##' @return A list of parameters of the distribution.
+##' @export
+##' @examples
+##' dist <- Gamma(shape = 3, rate = 2)
+##' get_parameters(dist)
+get_parameters <- function(x, id = NULL) {
+  if (!is(x, "dist_spec")) {
+    stop("Can only get parameters of a <dist_spec>.")
+  }
+  id <- get_dist_spec_id(x, id)
+  if (x[[id]]$distribution == "nonparametric") {
+    stop("Cannot get parameters of a nonparametric distribution.")
+  }
+  return(x[[id]]$parameters)
+}
+
+##' Get the probability mass function of a nonparametric distribution
+##'
+##' @inheritParams get_dist_spec_id
+##' @description `r lifecycle::badge("experimental")`
+##' @return The pmf of the distribution
+##' @export
+##' @examples
+##' dist <- discretise(Gamma(shape = 3, rate = 2, max = 10))
+##' get_pmf(dist)
+get_pmf <- function(x, id = NULL) {
+  if (!is(x, "dist_spec")) {
+    stop("Can only get pmf of a <dist_spec>.")
+  }
+  id <- get_dist_spec_id(x, id)
+  if (x[[id]]$distribution != "nonparametric") {
+    stop("Cannot get pmf of a parametric distribution.")
+  }
+  return(x[[id]]$pmf)
+}
+
+##' Get the distribution of a [dist_spec()]
+##'
+##' @inheritParams get_dist_spec_id
+##' @description `r lifecycle::badge("experimental")`
+##' @return A character string naming the distribution (or "nonparametric")
+##' @export
+##' @examples
+##' dist <- Gamma(shape = 3, rate = 2, max = 10)
+##' get_distribution(dist)
+get_distribution <- function(x, id = NULL) {
+  if (!is(x, "dist_spec")) {
+    stop("Can only get distribution of a <dist_spec>.")
+  }
+  id <- get_dist_spec_id(x, id)
+  return(x[[id]]$distribution)
+}

R/estimate_truncation.R

@@ -105,7 +105,7 @@
 #' # illustrative purposes only.
 #' out <- epinow(
 #'   example_truncated[[5]],
-#'   truncation = est$dist
+#'   truncation = trunc_opts(est$dist)
 #' )
 #' plot(out)
 #' options(old_opts)
@@ -291,9 +291,12 @@ estimate_truncation <- function(data, max_truncation, trunc_max = 10,
   parameters <- purrr::map(seq_along(params_mean), function(id) {
     Normal(params_mean[id], params_sd[id])
   })
-  names(parameters) <- natural_params(truncation[[1]]$distribution)
-  out$dist <- truncation
-  out$dist[[1]]$parameters <- parameters
+  names(parameters) <- natural_params(get_distribution(truncation))
+  parameters$max <- max(truncation)
+  out$dist <- new_dist_spec(
+    params = parameters,
+    distribution = get_distribution(truncation)
+  )
 
   # summarise reconstructed observations
   recon_obs <- extract_stan_param(fit, "recon_obs",

_pkgdown.yml

@@ -126,7 +126,11 @@ reference:
       - apply_tolerance
       - collapse
       - discretise
-      - contains("dist")
+      - contains("_dist")
+      - contains("dist_")
+      - get_parameters
+      - get_pmf
+      - get_distribution
   - title: Simulate
     desc: Functions to help with simulating data or mapping to reported cases
     contents:

data-raw/truncated.R

@@ -11,7 +11,7 @@ library("EpiNow2")
 #' @keywords internal
 apply_truncation <- function(index, data, dist) {
   set.seed(index)
-  if (dist[[1]]$distribution == 0) {
+  if (get_distribution(dist) == "lognormal") {
     dfunc <- dlnorm
   } else {
     dfunc <- dgamma
@@ -20,12 +20,12 @@ apply_truncation <- function(index, data, dist) {
     dfunc(
       seq_len(max(dist) + 1),
       rnorm(1,
-        dist[[1]]$parameters$meanlog[[1]]$parameters$mean,
-        dist[[1]]$parameters$meanlog[[1]]$parameters$sd
+        get_parameters(get_parameters(dist)$meanlog)$mean,
+        get_parameters(get_parameters(dist)$meanlog)$sd
       ),
       rnorm(1,
-        dist[[1]]$parameters$sdlog[[1]]$parameters$mean,
-        dist[[1]]$parameters$sdlog[[1]]$parameters$sd
+        get_parameters(get_parameters(dist)$sdlog)$mean,
+        get_parameters(get_parameters(dist)$sdlog)$sd
       )
     )
   )