Some improvements for borderline cases

- fit_with_errors for saem()
- test_log_parms for mean_degparms() and saem()

1 files changed, 34 insertions, 3 deletions

diff --git a/R/nlme.R b/R/nlme.R
index 9215aab0..d235a094 100644
--- a/R/nlme.R
+++ b/R/nlme.R
@@ -36,7 +36,7 @@
 #' nlme_f <- nlme_function(f)
 #' # These assignments are necessary for these objects to be
 #' # visible to nlme and augPred when evaluation is done by
-#' # pkgdown to generated the html docs.
+#' # pkgdown to generate the html docs.
 #' assign("nlme_f", nlme_f, globalenv())
 #' assign("grouped_data", grouped_data, globalenv())
 #'
@@ -130,13 +130,44 @@ nlme_function <- function(object) {
 #'   fixed and random effects, in the format required by the start argument of
 #'   nlme for the case of a single grouping variable ds.
 #' @param random Should a list with fixed and random effects be returned?
+#' @param test_log_parms If TRUE, log parameters are only considered in
+#'   the mean calculations if their untransformed counterparts (most likely
+#'   rate constants) pass the t-test for significant difference from zero.
+#' @param conf.level Possibility to adjust the required confidence level
+#'   for parameter that are tested if requested by 'test_log_parms'.
 #' @export
-mean_degparms <- function(object, random = FALSE) {
+mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
+{
   if (nrow(object) > 1) stop("Only row objects allowed")
   parm_mat_trans <- sapply(object, parms, transformed = TRUE)
+
+  if (test_log_parms) {
+      parm_mat_dim <- dim(parm_mat_trans)
+      parm_mat_dimnames <- dimnames(parm_mat_trans)
+
+      log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE)
+      log_parm_names <- gsub("^log_", "", log_parm_trans_names)
+
+      t_test_back_OK <- matrix(
+        sapply(object, function(o) {
+          suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(>t)"] < (1 - conf.level))
+        }), nrow = length(log_parm_names))
+      rownames(t_test_back_OK) <- log_parm_trans_names
+
+      parm_mat_trans_OK <- parm_mat_trans
+      for (trans_parm in log_parm_trans_names) {
+        parm_mat_trans_OK[trans_parm, ] <- ifelse(t_test_back_OK[trans_parm, ],
+          parm_mat_trans[trans_parm, ], NA)
+      }
+    } else {
+    parm_mat_trans_OK <- parm_mat_trans
+  }
+
   mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms))
   degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE]
-  fixed <- apply(degparm_mat_trans, 1, mean)
+  degparm_mat_trans_OK <- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE]
+
+  fixed <- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE)
   if (random) {
     random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed))
     # If we only have one parameter, apply returns a vector so we get a single row