distribution interface to dist_spec

NAMESPACE

@@ -1,6 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method("+",dist_spec)
+S3method(c,dist_spec)
+S3method(max,dist_spec)
 S3method(mean,dist_spec)
 S3method(plot,dist_spec)
 S3method(plot,epinow)
@@ -10,16 +12,23 @@ S3method(plot,estimate_truncation)
 S3method(print,dist_spec)
 S3method(summary,epinow)
 S3method(summary,estimate_infections)
+export(Fixed)
+export(Gamma)
+export(LogNormal)
+export(NonParametric)
+export(Normal)
 export(R_to_growth)
 export(add_day_of_week)
 export(adjust_infection_to_report)
+export(apply_tolerance)
 export(backcalc_opts)
 export(bootstrapped_dist_fit)
 export(calc_CrI)
 export(calc_CrIs)
 export(calc_summary_measures)
 export(calc_summary_stats)
 export(clean_nowcasts)
+export(collapse)
 export(construct_output)
 export(convert_to_logmean)
 export(convert_to_logsd)
@@ -35,6 +44,8 @@ export(create_shifted_cases)
 export(create_stan_args)
 export(create_stan_data)
 export(delay_opts)
+export(discretise)
+export(discretize)
 export(dist_fit)
 export(dist_skel)
 export(dist_spec)
@@ -195,16 +206,19 @@ importFrom(posterior,mcse_mean)
 importFrom(progressr,progressor)
 importFrom(progressr,with_progress)
 importFrom(purrr,compact)
+importFrom(purrr,flatten)
 importFrom(purrr,keep)
 importFrom(purrr,list_transpose)
 importFrom(purrr,map)
 importFrom(purrr,map2_dbl)
 importFrom(purrr,map_chr)
 importFrom(purrr,map_dbl)
+importFrom(purrr,map_dfc)
 importFrom(purrr,pmap_dbl)
 importFrom(purrr,quietly)
 importFrom(purrr,reduce)
 importFrom(purrr,safely)
+importFrom(purrr,transpose)
 importFrom(purrr,walk)
 importFrom(rlang,abort)
 importFrom(rlang,arg_match)

NEWS.md

@@ -9,6 +9,7 @@
 * `simulate_infections` has been renamed to `forecast_infections` in line with `simulate_secondary` and `forecast_secondary`. The terminology is: a forecast is done from a fit to existing data, a simulation from first principles. By @sbfnk in #544 and reviewed by @seabbs.
 * 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.
 
 ## Documentation
 

R/adjust.R

@@ -85,6 +85,7 @@ adjust_infection_to_report <- function(infections, delay_defs,
   # Reset DT Defaults on Exit
   set_dt_single_thread()
 
+  ## deprecated
   sample_single_dist <- function(input, delay_def) {
     ## Define sample delay fn
     sample_delay_fn <- function(n, ...) {
@@ -111,14 +112,50 @@ adjust_infection_to_report <- function(infections, delay_defs,
     return(out)
   }
 
-  report <- sample_single_dist(infections, delay_defs[[1]])
-
-  if (length(delay_defs) > 1) {
-    for (def in 2:length(delay_defs)) {
-      report <- sample_single_dist(report, delay_defs[[def]])
+  sample_dist_spec <- function(input, delay_def) {
+    ## Define sample delay fn
+    sample_delay_fn <- function(n, dist, cum, ...) {
+      fixed_dist <- discretise(fix_dist(delay_def, strategy = "sample"))
+      if (dist) {
+        fixed_dist[[1]]$pmf[n + 1]
+      } else {
+        sample(seq_along(fixed_dist[[1]]$pmf) - 1, size = n, replace = TRUE)
+      }
     }
+
+    ## Infection to onset
+    out <- EpiNow2::sample_approx_dist(
+      cases = input,
+      dist_fn = sample_delay_fn,
+      max_value = max(delay_def),
+      direction = "forwards",
+      type = type,
+      truncate_future = FALSE
+    )
+
+    return(out)
   }
 
+  if (is(delay_defs, "dist_spec")) {
+    report <- sample_dist_spec(infections, extract_single_dist(delay_defs, 1))
+    if (length(delay_defs) > 1) {
+      for (def in seq(2, length(delay_defs))) {
+        report <- sample_dist_spec(report, extract_single_dist(delay_defs, def))
+      }
+    }
+  } else {
+    deprecate_warn(
+      "1.5.0",
+      "adjust_infection_to_report(delay_defs = 'should be a dist_spec')",
+      details = "Specifying this as a list of data tables is deprecated."
+    )
+    report <- sample_single_dist(infections, delay_defs[[1]])
+    if (length(delay_defs) > 1) {
+      for (def in 2:length(delay_defs)) {
+        report <- sample_single_dist(report, delay_defs[[def]])
+      }
+    }
+  }
   ## Add a weekly reporting effect if present
   if (!missing(reporting_effect)) {
     reporting_effect <- data.table::data.table(
@@ -146,3 +183,188 @@ adjust_infection_to_report <- function(infections, delay_defs,
   }
   return(report)
 }
+
+#' Approximate Sampling a Distribution using Counts
+#'
+#' @description `r lifecycle::badge("soft-deprecated")`
+#' Convolves cases by a PMF function. This function will soon be removed or
+#' replaced with a more robust stan implementation.
+#'
+#' @param cases A `<data.frame>` of cases (in date order) with the following
+#' variables: `date` and `cases`.
+#'
+#' @param max_value Numeric, maximum value to allow. Defaults to 120 days
+#'
+#' @param direction Character string, defato "backwards". Direction in which to
+#' map cases. Supports either "backwards" or "forwards".
+#'
+#' @param dist_fn Function that takes two arguments with the first being
+#' numeric and the second being logical (and defined as `dist`). Should return
+#' the probability density or a sample from the defined distribution. See
+#' the examples for more.
+#'
+#' @param earliest_allowed_mapped A character string representing a date
+#' ("2020-01-01"). Indicates the earliest allowed mapped value.
+#'
+#' @param type Character string indicating the method to use to transform
+#' counts. Supports either "sample"  which approximates sampling or "median"
+#' would shift by the median of the distribution.
+#'
+#' @param truncate_future Logical, should cases be truncated if they occur
+#' after the first date reported in the data. Defaults to `TRUE`.
+#'
+#' @return A `<data.table>` of cases by date of onset
+#' @export
+#' @importFrom purrr map_dfc
+#' @importFrom data.table data.table setorder
+#' @importFrom lubridate days
+#' @examples
+#' \donttest{
+#' cases <- example_confirmed
+#' cases <- cases[, cases := as.integer(confirm)]
+#' print(cases)
+#'
+#' # total cases
+#' sum(cases$cases)
+#'
+#' delay_fn <- function(n, dist, cum) {
+#'   if (dist) {
+#'     pgamma(n + 0.9999, 2, 1) - pgamma(n - 1e-5, 2, 1)
+#'   } else {
+#'     as.integer(rgamma(n, 2, 1))
+#'   }
+#' }
+#'
+#' onsets <- sample_approx_dist(
+#'   cases = cases,
+#'   dist_fn = delay_fn
+#' )
+#'
+#' # estimated onset distribution
+#' print(onsets)
+#'
+#' # check that sum is equal to reported cases
+#' total_onsets <- median(
+#'   purrr::map_dbl(
+#'     1:10,
+#'     ~ sum(sample_approx_dist(
+#'       cases = cases,
+#'       dist_fn = delay_fn
+#'     )$cases)
+#'   )
+#' )
+#' total_onsets
+#'
+#'
+#' # map from onset cases to reported
+#' reports <- sample_approx_dist(
+#'   cases = cases,
+#'   dist_fn = delay_fn,
+#'   direction = "forwards"
+#' )
+#'
+#'
+#' # map from onset cases to reported using a mean shift
+#' reports <- sample_approx_dist(
+#'   cases = cases,
+#'   dist_fn = delay_fn,
+#'   direction = "forwards",
+#'   type = "median"
+#' )
+#' }
+sample_approx_dist <- function(cases = NULL,
+                               dist_fn = NULL,
+                               max_value = 120,
+                               earliest_allowed_mapped = NULL,
+                               direction = "backwards",
+                               type = "sample",
+                               truncate_future = TRUE) {
+  if (type == "sample") {
+    if (direction == "backwards") {
+      direction_fn <- rev
+    } else if (direction == "forwards") {
+      direction_fn <- function(x) {
+        x
+      }
+    }
+    # reverse cases so starts with current first
+    reversed_cases <- direction_fn(cases$cases)
+    reversed_cases[is.na(reversed_cases)] <- 0
+    # draw from the density fn of the dist
+    draw <- dist_fn(0:max_value, dist = TRUE, cum = FALSE)
+
+    # approximate cases
+    mapped_cases <- do.call(cbind, purrr::map(
+      seq_along(reversed_cases),
+      ~ c(
+        rep(0, . - 1),
+        stats::rbinom(
+          length(draw),
+          rep(reversed_cases[.], length(draw)),
+          draw
+        ),
+        rep(0, length(reversed_cases) - .)
+      )
+    ))
+
+
+    # set dates order based on direction mapping
+    if (direction == "backwards") {
+      dates <- seq(min(cases$date) - lubridate::days(length(draw) - 1),
+        max(cases$date),
+        by = "days"
+      )
+    } else if (direction == "forwards") {
+      dates <- seq(min(cases$date),
+        max(cases$date) + lubridate::days(length(draw) - 1),
+        by = "days"
+      )
+    }
+
+    # summarises movements and sample for placement of non-integer cases
+    case_sum <- direction_fn(rowSums(mapped_cases))
+    floor_case_sum <- floor(case_sum)
+    sample_cases <- floor_case_sum +
+      as.numeric((runif(seq_along(case_sum)) < (case_sum - floor_case_sum)))
+
+    # summarise imputed onsets and build output data.table
+    mapped_cases <- data.table::data.table(
+      date = dates,
+      cases = sample_cases
+    )
+
+    # filter out all zero cases until first recorded case
+    mapped_cases <- data.table::setorder(mapped_cases, date)
+    mapped_cases <- mapped_cases[
+      ,
+      cum_cases := cumsum(cases)
+    ][cum_cases != 0][, cum_cases := NULL]
+  } else if (type == "median") {
+    shift <- as.integer(
+      median(as.integer(dist_fn(1000, dist = FALSE)), na.rm = TRUE)
+    )
+
+    if (direction == "backwards") {
+      mapped_cases <- data.table::copy(cases)[
+        ,
+        date := date - lubridate::days(shift)
+      ]
+    } else if (direction == "forwards") {
+      mapped_cases <- data.table::copy(cases)[
+        ,
+        date := date + lubridate::days(shift)
+      ]
+    }
+  }
+
+  if (!is.null(earliest_allowed_mapped)) {
+    mapped_cases <- mapped_cases[date >= as.Date(earliest_allowed_mapped)]
+  }
+
+  # filter out future cases
+  if (direction == "forwards" && truncate_future) {
+    max_date <- max(cases$date)
+    mapped_cases <- mapped_cases[date <= max_date]
+  }
+  return(mapped_cases)
+}

R/checks.R

@@ -53,3 +53,53 @@ check_reports_valid <- function(reports, model) {
     assert_numeric(reports$confirm, lower = 0)
   }
 }
+
+#' Validate probability distribution for passing to stan
+#'
+#' @description
+#' `check_stan_delay()` checks that the supplied data is a `<dist_spec>`,
+#' that it is a supported distribution, and that is has a finite maximum.
+#'
+#' @param dist A `dist_spec` object.`
+#' @importFrom checkmate assert_class
+#' @importFrom rlang arg_match
+#' @return Called for its side effects.
+#' @keywords internal
+check_stan_delay <- function(dist) {
+  # Check that `dist` is a `dist_spec`
+  assert_class(dist, "dist_spec")
+  # Check that `dist` is lognormal or gamma or nonparametric
+  distributions <- vapply(dist, function(x) x$distribution, character(1))
+  if (
+    !all(distributions %in% c("lognormal", "gamma", "fixed", "nonparametric"))
+  ) {
+    stop(
+      "Distributions passed to the model need to be lognormal, gamma, fixed ",
+      "or nonparametric."
+    )
+  }
+  # Check that `dist` has parameters that are either numeric or normal
+  # distributions with numeric parameters and infinite maximum
+  numeric_parameters <- vapply(dist$parameters, is.numeric, logical(1))
+  normal_parameters <- vapply(
+    dist$parameters,
+    function(x) {
+      is(x, "dist_spec") &&
+        x$distribution == "normal" &&
+        all(vapply(x$parameters, is.numeric, logical(1))) &&
+        is.infinite(x$max)
+    },
+    logical(1)
+  )
+  if (!all(numeric_parameters | normal_parameters)) {
+    stop(
+      "Delay distributions passed to the model need to have parameters that ",
+      "are either numeric or normally distributed with numeric parameters ",
+      "and infinite maximum."
+    )
+  }
+  # Check that `dist` has a finite maximum
+  if (any(is.infinite(max(dist)))) {
+    stop("All distribution passed to the model need to have a finite maximum")
+  }
+}