add conditional arg for predict

NEWS.md

@@ -1,5 +1,13 @@
 # mmrm 0.3.11.9000
 
+### New Features
+
+- Add parameter `conditional` for `predict` method to control whether the prediction is conditional on the observation or not.
+
+### Bug Fixes
+
+- Previously if the left hand side of a model formula is an expression, `predict` and `simulate` will fail. This is fixed now.
+
 # mmrm 0.3.11
 
 ### Bug Fixes

R/tmb-methods.R

@@ -54,6 +54,7 @@ fitted.mmrm_tmb <- function(object, ...) {
 #' @param interval (`string`)\cr type of interval calculation. Can be abbreviated.
 #' @param level (`number`)\cr tolerance/confidence level.
 #' @param nsim (`count`)\cr number of simulations to use.
+#' @param conditional (`flag`)\cr indicator if the prediction is conditional on the observation or not.
 #'
 #' @importFrom stats predict
 #' @exportS3Method
@@ -66,6 +67,7 @@ predict.mmrm_tmb <- function(object,
                              interval = c("none", "confidence", "prediction"),
                              level = 0.95,
                              nsim = 1000L,
+                             conditional = TRUE,
                              ...) {
   if (missing(newdata)) {
     newdata <- object$data
@@ -75,23 +77,22 @@ predict.mmrm_tmb <- function(object,
   assert_flag(se.fit)
   assert_number(level, lower = 0, upper = 1)
   assert_count(nsim, positive = TRUE)
+  assert_flag(conditional)
   interval <- match.arg(interval)
   # make sure new data has the same levels as original data
-  full_frame <- model.frame(
-    object,
-    data = newdata,
-    include = c("subject_var", "visit_var", "group_var", "response_var"),
-    na.action = "na.pass"
-  )
+  newdata <- h_factor_ref_data(object, newdata)
   tmb_data <- h_mmrm_tmb_data(
-    object$formula_parts, full_frame,
-    weights = rep(1, nrow(full_frame)),
+    object$formula_parts, newdata,
+    weights = rep(1, nrow(newdata)),
     reml = TRUE,
     singular = "keep",
     drop_visit_levels = FALSE,
     allow_na_response = TRUE,
     drop_levels = FALSE
   )
+  if (!conditional) {
+    tmb_data$y_vector[] <- NA_real_
+  }
   if (any(object$tmb_data$x_cols_aliased)) {
     warning(
       "In fitted object there are co-linear variables and therefore dropped terms, ",
@@ -610,15 +611,10 @@ simulate.mmrm_tmb <- function(object,
   method <- match.arg(method)
 
   # Ensure new data has the same levels as original data.
-  full_frame <- model.frame(
-    object,
-    data = newdata,
-    include = c("subject_var", "visit_var", "group_var", "response_var"),
-    na.action = "na.pass"
-  )
+  newdata <- h_factor_ref_data(object, newdata)
   tmb_data <- h_mmrm_tmb_data(
-    object$formula_parts, full_frame,
-    weights = rep(1, nrow(full_frame)),
+    object$formula_parts, newdata,
+    weights = rep(1, nrow(newdata)),
     reml = TRUE,
     singular = "keep",
     drop_visit_levels = FALSE,

R/tmb.R

@@ -18,6 +18,7 @@
 #' - `subject_var`: `string` with the subject variable name.
 #' - `group_var`: `string` with the group variable name. If no group specified,
 #'   this element is `NULL`.
+#' - `model_var`: `character` with the variables names of the formula, except `subject_var`.
 #'
 #' @keywords internal
 h_mmrm_tmb_formula_parts <- function(
@@ -37,7 +38,8 @@ h_mmrm_tmb_formula_parts <- function(
       is_spatial = covariance$type == "sp_exp",
       visit_var = covariance$visits,
       subject_var = covariance$subject,
-      group_var = if (length(covariance$group) < 1) NULL else covariance$group
+      group_var = if (length(covariance$group) < 1) NULL else covariance$group,
+      model_var = setdiff(all.vars(formula[[3]]), covariance$subject)
     ),
     class = "mmrm_tmb_formula_parts"
   )
@@ -135,28 +137,27 @@ h_mmrm_tmb_data <- function(formula_parts,
   # Weights is always the last column.
   weights_name <- colnames(data)[ncol(data)]
   # If `y` is allowed to be NA, then first replace y with 1:n, then replace it with original y.
-  if (allow_na_response) {
-    y_original <- eval(formula_parts$full_formula[[2]], envir = data)
-    vn <- deparse(formula_parts$full_formula[[2]])
-    data[[vn]] <- seq_len(nrow(data))
-  } else {
+  if (!allow_na_response) {
     h_warn_na_action()
   }
   full_frame <- eval(
     bquote(stats::model.frame(
       formula_parts$full_formula,
       data = data,
       weights = .(as.symbol(weights_name)),
-      na.action = stats::na.omit
+      na.action = "na.pass"
     ))
   )
   if (drop_levels) {
     full_frame <- droplevels(full_frame, except = formula_parts$visit_var)
   }
-  # If `y` is allowed to be NA, replace it with original y.
   if (allow_na_response) {
-    full_frame[[vn]] <- y_original[full_frame[[vn]]]
+    # response is always the first column
+    keep_ind <- complete.cases(full_frame[, -1L, drop = FALSE])
+  } else {
+    keep_ind <- complete.cases(full_frame)
   }
+  full_frame <- full_frame[keep_ind, ]
   if (drop_visit_levels && !formula_parts$is_spatial && is.factor(full_frame[[formula_parts$visit_var]])) {
     old_levels <- levels(full_frame[[formula_parts$visit_var]])
     full_frame[[formula_parts$visit_var]] <- droplevels(full_frame[[formula_parts$visit_var]])
@@ -166,6 +167,7 @@ h_mmrm_tmb_data <- function(formula_parts,
       message("In ", formula_parts$visit_var, " there are dropped visits: ", toString(dropped))
     }
   }
+
   x_matrix <- stats::model.matrix(formula_parts$model_formula, data = full_frame)
   x_cols_aliased <- stats::setNames(rep(FALSE, ncol(x_matrix)), nm = colnames(x_matrix))
   qr_x_mat <- qr(x_matrix)
@@ -186,7 +188,6 @@ h_mmrm_tmb_data <- function(formula_parts,
       attr(x_matrix, "contrasts") <- contrasts_attr
     }
   }
-
   y_vector <- as.numeric(stats::model.response(full_frame))
   weights_vector <- as.numeric(stats::model.weights(full_frame))
   n_subjects <- length(unique(full_frame[[formula_parts$subject_var]]))

R/utils.R

@@ -215,6 +215,7 @@ h_partial_fun_args <- function(fun, ..., additional_attr = list()) {
 #' For "Kenward-Roger" only, "Kenward-Roger" is returned.
 #' For "Residual" only, "Empirical" is returned.
 #'
+#' @return String of the default covariance method.
 #' @keywords internal
 h_get_cov_default <- function(method = c("Satterthwaite", "Kenward-Roger", "Residual", "Between-Within")) {
   assert_string(method)
@@ -264,6 +265,7 @@ drop_elements <- function(x, n) {
 #'
 #' @param x (`numeric`)\cr number of visit levels.
 #'
+#' @return Logical value `TRUE`.
 #' @keywords internal
 h_confirm_large_levels <- function(x) {
   assert_count(x)
@@ -314,6 +316,7 @@ h_default_value <- function(x, y) {
 #' This is needed even if `x` and `ref` are both `character` because
 #' in `model.matrix` if `x` only has one level there could be errors.
 #'
+#' @return Factor vector with updated levels.
 #' @keywords internal
 h_factor_ref <- function(x, ref, var_name = vname(x)) {
   assert_multi_class(ref, c("character", "factor"))
@@ -327,6 +330,30 @@ h_factor_ref <- function(x, ref, var_name = vname(x)) {
   factor(x, levels = h_default_value(levels(ref), sort(uni_ref)))
 }
 
+#' Convert Character to Factor Following Reference `MMRM` Fit.
+#'
+#' @param object (`mmrm_tmb`)\cr the fitted MMRM object.
+#' @param data (`data.frame`)\cr input data.
+#'
+#' @details Use fitted mmrm object to convert input data frame whose factors
+#' are of the same levels as the reference fitted object.
+#'
+#' @return Data frame with updated levels in specified columns.
+#' @keywords internal
+h_factor_ref_data <- function(object, data) {
+  assert_data_frame(data)
+  assert_class(object, "mmrm_tmb")
+  ref <- object$tmb_data$full_frame
+  vars <- object$formula_parts$model_var
+
+  for (v in vars) {
+    if (is.factor(ref[[v]]) || is.character(ref[[v]])) {
+      data[[v]] <- h_factor_ref(data[[v]], ref[[v]])
+    }
+  }
+  data
+}
+
 #' Warn on na.action
 #' @keywords internal
 h_warn_na_action <- function() {
@@ -446,6 +473,7 @@ emp_start <- function(data, model_formula, visit_var, subject_var, subject_group
 #' If the covariance matrix has `NA` in some of the elements, they will be replaced by
 #' 0 (non-diagonal) and 1 (diagonal). This ensures that the matrix is positive definite.
 #'
+#' @return Numeric vector of the theta values.
 #' @keywords internal
 h_get_theta_from_cov <- function(covariance) {
   assert_matrix(covariance, mode = "numeric", ncols = nrow(covariance))

tests/testthat/test-tmb-methods.R

@@ -173,6 +173,41 @@ test_that("predict will return NA if data contains NA in covariates", {
   )
 })
 
+test_that("predict can give unconditional predictions", {
+  fit <- get_mmrm()
+  expect_silent(p <- predict(fit, newdata = fev_data, conditional = FALSE))
+  m <- stats::model.matrix(
+    fit$formula_parts$model_formula,
+    model.frame(fit, data = fev_data, include = "response_var", na.action = "na.pass")
+  )
+  expect_equal(
+    p,
+    (m %*% fit$beta_est)[, 1],
+    tolerance = 1e-7
+  )
+})
+
+test_that("predict can change based on coefficients", {
+  fit <- get_mmrm()
+  new_beta <- coef(fit) + 0.1
+  fit$beta_est <- new_beta
+  m <- stats::model.matrix(
+    fit$formula_parts$model_formula,
+    model.frame(fit, data = fev_data, include = "response_var", na.action = "na.pass")
+  )
+  expect_silent(p <- predict(fit, newdata = fev_data, conditional = FALSE))
+  expect_equal(
+    p,
+    (m %*% new_beta)[, 1],
+    tolerance = 1e-7
+  )
+})
+
+test_that("predict can work if response is an expression", {
+  fit <- mmrm(log(FEV1) + FEV1 ~ ARMCD * AVISIT + ar1(AVISIT | USUBJID), data = fev_data)
+  expect_silent(p <- predict(fit, newdata = fev_data, conditional = FALSE))
+})
+
 ## integration test with SAS ----
 
 test_that("predict gives same result with sas in unstructured satterthwaite/Kenward-Roger", {
@@ -865,6 +900,13 @@ test_that("response residuals helper function works as expected", {
 
 # simulate.mmrm_tmb ----
 
+test_that("simulate works if the model reponse is an expression", {
+  object <- mmrm(log(FEV1) + FEV1 ~ ARMCD * AVISIT + ar1(AVISIT | USUBJID), data = fev_data)
+  set.seed(1001)
+  sims <- simulate(object, nsim = 2, method = "conditional")
+  expect_data_frame(sims, any.missing = FALSE, nrows = nrow(object$data), ncols = 2)
+})
+
 test_that("simulate with conditional method returns a df of correct dimension", {
   object <- get_mmrm()
   set.seed(1001)

tests/testthat/test-tmb.R

@@ -35,7 +35,8 @@ test_that("h_mmrm_tmb_formula_parts works as expected", {
       is_spatial = FALSE,
       visit_var = "AVISIT",
       subject_var = "USUBJID",
-      group_var = NULL
+      group_var = NULL,
+      model_var = c("RACE", "SEX", "ARMCD", "AVISIT")
     ),
     class = "mmrm_tmb_formula_parts"
   )
@@ -53,7 +54,8 @@ test_that("h_mmrm_tmb_formula_parts works as expected", {
       is_spatial = FALSE,
       visit_var = "AVISIT",
       subject_var = "USUBJID",
-      group_var = "ARMCD"
+      group_var = "ARMCD",
+      model_var = c("RACE", "SEX", "ARMCD", "AVISIT")
     ),
     class = "mmrm_tmb_formula_parts"
   )
@@ -115,7 +117,8 @@ test_that("h_mmrm_tmb_formula_parts works without covariates", {
       is_spatial = FALSE,
       visit_var = "AVISIT",
       subject_var = "USUBJID",
-      group_var = NULL
+      group_var = NULL,
+      model_var = c("AVISIT")
     ),
     class = "mmrm_tmb_formula_parts"
   )
@@ -135,7 +138,8 @@ test_that("h_mmrm_tmb_formula_parts works as expected for antedependence", {
       is_spatial = FALSE,
       visit_var = "AVISIT",
       subject_var = "USUBJID",
-      group_var = NULL
+      group_var = NULL,
+      model_var = c("RACE", "SEX", "ARMCD", "AVISIT")
     ),
     class = "mmrm_tmb_formula_parts"
   )