feat: support categorical conditional filtering in sample_covariates_mice

For categorical covariates, the `conditional` argument now accepts a
vector of allowed category values (e.g. `list(SEX = c("F"))`) rather
than a min/max range. Continuous covariates retain the existing
`c(min, max)` range behavior.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: roninsightrx

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: irxforge
 Title: Forging data for pharmacometric analyses
-Version: 0.0.0.9001
+Version: 0.0.0.9002
 Authors@R: c(
     person("Ron", "Keizer", email = "ron@insight-rx.com", role = c("cre", "aut")),
     person("Michael", "McCarthy", email = "michael.mccarthy@insight-rx.com", role = "ctb"),

R/sample_covariates.R

@@ -1,4 +1,11 @@
 #' Sample covariates using a variety of methods
+#' 
+#' Categorical covariates: all covariate sampling methods (except multi-variate
+#' normal) support sampling of categorical covariates as well as continuous. 
+#' In the `mice` sampling method, the categorical covariates have to be
+#' provided specifically as a vector of `character` indicating the column names.
+#' If not provided, they will otherwise be treated as continuous variables and
+#' non-integer values may be sampled.
 #'
 #' @param method sampling method, one of `mvtnorm`, `bootstrap`, `mice`, or
 #' `nhanes`. E.g. `list(AGE = c(60, 80), WT = c(70, 100))`.

R/sample_covariates_mice.R

@@ -1,5 +1,9 @@
 #' Sample covariates using multivariate imputation using chained equations
 #' (mice)
+#' 
+#' In contrast to sampling methods bootstrap and NHANES, categorical covariates
+#' need to be specified using the `cat_covs` argument, otherwise they will be 
+#' treated as continuous.
 #'
 #' @param data data.frame (n x p) containing the original, observed,
 #' time-invariant covariates (ID should not be included) that will be used to
@@ -8,8 +12,11 @@
 #' covariates in orgCovs.
 #' @param n_subjects number of simulated subjects, default is the number of
 #' subjects in the data.
-#' @param conditional list with conditional limits for sampled population, e.g. 
-#' `list("WT" = c(40, 60), "BMI" = c(15, 25))`.
+#' @param conditional list with conditional limits for sampled population. For
+#' continuous covariates, specify a numeric vector of length 2 giving the
+#' `c(min, max)` range, e.g. `list("WT" = c(40, 60), "BMI" = c(15, 25))`.
+#' For categorical covariates (those listed in `cat_covs`), specify a character
+#' vector of the allowed category values, e.g. `list("SEX" = c("F"))`.
 #' @param cont_method method used to predict continuous covariates within mice,
 #' default is `pmm`.
 #' @param replicates number of multiple imputations replicates to sample.
@@ -61,11 +68,15 @@ sample_covariates_mice <- function(
     seed_covs <- names(conditional)
     pool_seed <- data
     for(key in seed_covs) {
-      pool_seed <- dplyr::filter(
-        pool_seed,
-        .data[[key]] >= min(conditional[[key]]) & 
-        .data[[key]] <= max(conditional[[key]])
-      ) 
+      if(key %in% cat_covs) {
+        pool_seed <- dplyr::filter(pool_seed, .data[[key]] %in% conditional[[key]])
+      } else {
+        pool_seed <- dplyr::filter(
+          pool_seed,
+          .data[[key]] >= min(conditional[[key]]) &
+          .data[[key]] <= max(conditional[[key]])
+        )
+      }
     }
     mi_data[seed_covs] <- pool_seed[
       sample(1:nrow(pool_seed), n_subjects, replace = T), seed_covs

man/sample_covariates.Rd

@@ -60,6 +60,23 @@ test_that("n_subjects controls the number of simulated rows", {
   expect_equal(nrow(out), 2)
 })
 
+test_that("conditional argument filters categorical covariates by value", {
+  dat <- data.frame(
+    AGE = c(20, 30, 40, 50, 60),
+    WT  = c(45, 55, 65, 75, 85),
+    SEX = c("M", "F", "F", "M", "F")
+  )
+  cndl <- list(SEX = c("F"))
+  out <- sample_covariates_mice(
+    data = dat,
+    cat_covs = "SEX",
+    conditional = cndl,
+    n_subjects = 5,
+    seed = 1
+  )
+  expect_true(all(as.character(out$SEX) == "F"))
+})
+
 test_that("conditional argument filters data before sampling", {
   dat <- data.frame(
     AGE = c(20, 30, 40, 50, 60),