Merge 8f76a2f into 669be3b

DESCRIPTION

@@ -149,7 +149,7 @@ Encoding: UTF-8
 Language: en-GB
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.0
 NeedsCompilation: yes
 SystemRequirements: GNU make
        C++17

NAMESPACE

@@ -129,6 +129,7 @@ importFrom(data.table,fwrite)
 importFrom(data.table,getDTthreads)
 importFrom(data.table,melt)
 importFrom(data.table,merge.data.table)
+importFrom(data.table,nafill)
 importFrom(data.table,rbindlist)
 importFrom(data.table,setDT)
 importFrom(data.table,setDTthreads)

NEWS.md

@@ -26,6 +26,7 @@
 ## Model changes
 
 * Updated the parameterisation of the dispersion term `phi` to be `phi = 1 / sqrt_phi ^ 2` rather than the previous parameterisation `phi = 1 / sqrt(sqrt_phi)` based on the suggested prior [here](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations#story-when-the-generic-prior-fails-the-case-of-the-negative-binomial) and the performance benefits seen in the `epinowcast` package (see [here](https://github.com/epinowcast/epinowcast/blob/8eff560d1fd8305f5fb26c21324b2bfca1f002b4/inst/stan/epinowcast.stan#L314)). By @seabbs in # and reviewed by @sbfnk.
+* Added an `na` argument to `obs_opts()` that allows the user to specify whether NA values in the data should be interpreted as missing or accumulated in the next non-NA data point. By @sbfnk in #534 and reviewed by @seabbs.
 
 # EpiNow2 1.4.0
 

R/create.R

@@ -26,7 +26,7 @@
 #' @export
 #' @examples
 #' create_clean_reported_cases(example_confirmed, 7)
-create_clean_reported_cases <- function(reported_cases, horizon,
+create_clean_reported_cases <- function(reported_cases, horizon = 0,
                                         filter_leading_zeros = TRUE,
                                         zero_threshold = Inf,
                                         fill = NA_integer_) {
@@ -75,6 +75,25 @@ create_clean_reported_cases <- function(reported_cases, horizon,
   return(reported_cases)
 }
 
+#' Create complete cases
+#' @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.
+#'
+#' @return A data frame without NA values, with two columns: confirm (number)
+#' @author Sebastian Funk
+#' @importFrom data.table setDT
+#' @keywords internal
+create_complete_cases <- function(cases) {
+  cases <- setDT(cases)
+  cases[, lookup := seq_len(.N)]
+  cases <- cases[!is.na(cases$confirm)]
+  return(cases[])
+}
+
 #' Create Delay Shifted Cases
 #'
 #' @description `r lifecycle::badge("stable")`
@@ -397,6 +416,7 @@ create_obs_model <- function(obs = obs_opts(), dates) {
     week_effect = ifelse(obs$week_effect, obs$week_length, 1),
     obs_weight = obs$weight,
     obs_scale = as.numeric(length(obs$scale) != 0),
+    accumulate = obs$accumulate,
     likelihood = as.numeric(obs$likelihood),
     return_likelihood = as.numeric(obs$return_likelihood)
   )
@@ -447,16 +467,13 @@ create_stan_data <- function(reported_cases, seeding_time,
                              backcalc, shifted_cases) {
 
   cases <- reported_cases[(seeding_time + 1):(.N - horizon)]
-  cases[, lookup := seq_len(.N)]
-  complete_cases <- cases[!is.na(cases$confirm)]
-  cases_time <- complete_cases$lookup
-  complete_cases <- complete_cases$confirm
+  complete_cases <- create_complete_cases(cases)
   cases <- cases$confirm
 
   data <- list(
-    cases = complete_cases,
-    cases_time = cases_time,
-    lt = length(cases_time),
+    cases = complete_cases$confirm,
+    cases_time = complete_cases$lookup,
+    lt = nrow(complete_cases),
     shifted_cases = shifted_cases,
     t = length(reported_cases$date),
     horizon = horizon,
@@ -481,7 +498,7 @@ create_stan_data <- function(reported_cases, seeding_time,
     is.na(data$prior_infections) || is.null(data$prior_infections),
     0, data$prior_infections
   )
-  if (data$seeding_time > 1) {
+  if (data$seeding_time > 1 && nrow(first_week) > 1) {
     safe_lm <- purrr::safely(stats::lm)
     data$prior_growth <- safe_lm(log(confirm) ~ t, data = first_week)[[1]]
     data$prior_growth <- ifelse(is.null(data$prior_growth), 0,

R/estimate_secondary.R

@@ -64,7 +64,7 @@
 #' @inheritParams calc_CrIs
 #' @importFrom rstan sampling
 #' @importFrom lubridate wday
-#' @importFrom data.table as.data.table merge.data.table
+#' @importFrom data.table as.data.table merge.data.table nafill
 #' @importFrom utils modifyList
 #' @importFrom checkmate assert_class assert_numeric assert_data_frame
 #' assert_logical
@@ -165,6 +165,15 @@ estimate_secondary <- function(reports,
   assert_logical(verbose)
 
   reports <- data.table::as.data.table(reports)
+  secondary_reports <- reports[, list(date, confirm = secondary)]
+  secondary_reports <- create_clean_reported_cases(secondary_reports)
+  ## fill in missing data (required if fitting to prevalence)
+  complete_secondary <- create_complete_cases(secondary_reports)
+
+  ## fill down
+  secondary_reports[, confirm := nafill(confirm, type = "locf")]
+  ## fill any early data up
+  secondary_reports[, confirm := nafill(confirm, type = "nocb")]
 
   if (burn_in >= nrow(reports)) {
     stop("burn_in is greater or equal to the number of observations.
@@ -173,8 +182,10 @@ estimate_secondary <- function(reports,
   # observation and control data
   data <- list(
     t = nrow(reports),
-    obs = reports$secondary,
     primary = reports$primary,
+    obs = secondary_reports$confirm,
+    obs_time = complete_secondary[lookup > burn_in]$lookup - burn_in,
+    lt = sum(complete_secondary$lookup > burn_in),
     burn_in = burn_in,
     seeding_time = 0
   )
@@ -395,7 +406,7 @@ plot.estimate_secondary <- function(x, primary = FALSE,
                                     from = NULL, to = NULL,
                                     new_obs = NULL,
                                     ...) {
-  predictions <- data.table::copy(x$predictions)
+  predictions <- data.table::copy(x$predictions)[!is.na(secondary)]
 
   if (!is.null(new_obs)) {
     new_obs <- data.table::as.data.table(new_obs)

R/opts.R

@@ -427,32 +427,35 @@ gp_opts <- function(basis_prop = 0.2,
 #' Defines a list specifying the structure of the observation
 #' model. Custom settings can be supplied which override the defaults.
 #' @param family Character string defining the observation model. Options are
-#' Negative binomial ("negbin"), the default, and Poisson.
-#' @param phi A numeric vector of length 2, defaults to 0, 1. Indicates the
-#' mean and standard deviation of the normal prior used for the observation
-#' process.
-#'
-#' @param weight Numeric, defaults to 1. Weight to give the observed data in
-#'  the log density.
+#'   Negative binomial ("negbin"), the default, and Poisson.
+#' @param phi A numeric vector of length 2, defaults to 0, 1. Indicates the mean
+#'   and standard deviation of the normal prior used for the observation
+#'   process.
+#' @param weight Numeric, defaults to 1. Weight to give the observed data in the
+#'   log density.
 #' @param week_effect Logical defaulting to `TRUE`. Should a day of the week
-#' effect be used in the observation model.
-#'
+#'   effect be used in the observation model.
 #' @param week_length Numeric assumed length of the week in days, defaulting to
 #' 7 days. This can be modified if data aggregated over a period other than a
 #' week or if data has a non-weekly periodicity.
-#'
 #' @param scale List, defaulting to an empty list. Should an scaling factor be
-#' applied to map latent infections (convolved to date of report). If none
-#' empty a mean (`mean`) and standard deviation (`sd`) needs to be supplied
-#' defining the normally distributed scaling factor.
-#'
+#'   applied to map latent infections (convolved to date of report). If none
+#'   empty a mean (`mean`) and standard deviation (`sd`) needs to be supplied
+#'   defining the normally distributed scaling factor.
+#' @param na Character. Options are "missing" (the default) and "accumulate".
+#'   This determines how NA values in the data are interpreted. If set to
+#'   "missing", any NA values in the observation data set will be interpreted as
+#'   missing and skipped in the likelihood. If set to "accumulate", modelled
+#'   observations will be accumulated and added to the next non-NA data point.
+#'   This can be used to model incidence data that is reported at less than
+#'   daily intervals. If set to "accumulate", the first data point is not
+#'   included in the likelihood but used only to reset modelled observations to
+#'   zero.
 #' @param likelihood Logical, defaults to `TRUE`. Should the likelihood be
-#' included in the model.
-#'
+#'   included in the model.
 #' @param return_likelihood Logical, defaults to `FALSE`. Should the likelihood
-#' be returned by the model.
+#'   be returned by the model.
 #' @importFrom rlang arg_match
-#'
 #' @return An `<obs_opts>` object of observation model settings.
 #' @author Sam Abbott
 #' @export
@@ -471,18 +474,32 @@ obs_opts <- function(family = "negbin",
                      week_effect = TRUE,
                      week_length = 7,
                      scale = list(),
+                     na = c("missing", "accumulate"),
                      likelihood = TRUE,
                      return_likelihood = FALSE) {
   if (length(phi) != 2 || !is.numeric(phi)) {
     stop("phi be numeric and of length two")
   }
+  na <- arg_match(na)
+  if (na == "accumulate") {
+    message(
+      "Accumulating modelled values that correspond to NA values in the data ",
+      "by adding them to the next non-NA data point. This means that the ",
+      "first data point is not included in the likelihood but used only to ",
+      "reset modelled observations to zero. If the first data point should be ",
+      "included in the likelihood this can be achieved by adding a data point ",
+      "of arbitrary value before the first data point."
+    )
+  }
+
   obs <- list(
     family = arg_match(family, values = c("poisson", "negbin")),
     phi = phi,
     weight = weight,
     week_effect = week_effect,
     week_length = week_length,
     scale = scale,
+    accumulate = as.integer(na == "accumulate"),
     likelihood = likelihood,
     return_likelihood = return_likelihood
   )

inst/stan/data/observation_model.stan

@@ -9,5 +9,6 @@
   real obs_weight;                   // weight given to observation in log density
   int likelihood;                    // Should the likelihood be included in the model
   int return_likelihood; // Should the likehood be returned by the model
+  int accumulate; // Should missing values be accumulated
   int<lower = 0> trunc_id; // id of truncation
   int<lower = 0> delay_id; // id of delay

inst/stan/estimate_infections.stan

@@ -148,8 +148,8 @@ model {
   // observed reports from mean of reports (update likelihood)
   if (likelihood) {
     report_lp(
-      cases, obs_reports[cases_time], rep_phi, phi_mean, phi_sd, model_type,
-      obs_weight
+      cases, cases_time, obs_reports, rep_phi, phi_mean, phi_sd, model_type,
+      obs_weight, accumulate
     );
   }
 }

inst/stan/estimate_secondary.stan

@@ -8,7 +8,9 @@ functions {
 
 data {
   int t;                             // time of observations
+  int lt;                             // time of observations
   array[t] int<lower = 0> obs;             // observed secondary data
+  array[lt] int obs_time;             // observed secondary data
   vector[t] primary;                 // observed primary data
   int burn_in;                       // time period to not use for fitting
 #include data/secondary.stan
@@ -83,8 +85,10 @@ model {
    }
   // observed secondary reports from mean of secondary reports (update likelihood)
   if (likelihood) {
-    report_lp(obs[(burn_in + 1):t], secondary[(burn_in + 1):t],
-              rep_phi, phi_mean, phi_sd, model_type, 1);
+    report_lp(
+      obs[(burn_in + 1):t][obs_time], obs_time, secondary[(burn_in + 1):t],
+      rep_phi, phi_mean, phi_sd, model_type, 1, accumulate
+    );
   }
 }
 

inst/stan/functions/observation_model.stan

@@ -51,22 +51,46 @@ void truncation_lp(array[] real truncation_mean, array[] real truncation_sd,
   }
 }
 // update log density for reported cases
-void report_lp(array[] int cases, vector reports,
+void report_lp(array[] int cases, array[] int cases_time, vector reports,
                array[] real rep_phi, real phi_mean, real phi_sd,
-               int model_type, real weight) {
+               int model_type, real weight, int accumulate) {
+  int n = num_elements(cases_time) - accumulate; // number of observations
+  vector[n] obs_reports; // reports at observation time
+  array[n] int obs_cases; // observed cases at observation time
+  if (accumulate) {
+    int t = num_elements(reports);
+    int i = 0;
+    int current_obs = 0;
+    obs_reports = rep_vector(0, n);
+    while (i <= t && current_obs <= n) {
+      if (current_obs > 0) { // first observation gets ignored when accumulating
+        obs_reports[current_obs] += reports[i];
+      }
+      if (i == cases_time[current_obs + 1]) {
+        current_obs += 1;
+      }
+      i += 1;
+    }
+    obs_cases = cases[2:(n + 1)];
+  } else {
+    obs_reports = reports[cases_time];
+    obs_cases = cases;
+  }
   if (model_type) {
-    real dispersion = 1 / pow(rep_phi[model_type], 2); 
+    real dispersion = 1 / pow(rep_phi[model_type], 2);
     rep_phi[model_type] ~ normal(phi_mean, phi_sd) T[0,];
     if (weight == 1) {
-      cases ~ neg_binomial_2(reports, dispersion);
+      obs_cases ~ neg_binomial_2(obs_reports, dispersion);
     } else {
-      target += neg_binomial_2_lpmf(cases | reports, dispersion) * weight;
+      target += neg_binomial_2_lpmf(
+        obs_cases | obs_reports, dispersion
+      ) * weight;
     }
   } else {
     if (weight == 1) {
-      cases ~ poisson(reports);
+      obs_cases ~ poisson(obs_reports);
     } else {
-      target += poisson_lpmf(cases | reports) * weight;
+      target += poisson_lpmf(obs_cases | obs_reports) * weight;
     }
   }
 }
@@ -97,7 +121,7 @@ array[] int report_rng(vector reports, array[] real rep_phi, int model_type) {
   if (model_type) {
     dispersion = 1 / pow(rep_phi[model_type], 2);
   }
-    
+
   for (s in 1:t) {
     if (reports[s] < 1e-8) {
       sampled_reports[s] = 0;