Finished 'summary.nlmixr.mmkin', checks, docs

4 files changed, 51 insertions, 35 deletions

diff --git a/R/endpoints.R b/R/endpoints.R
index f1f47581..6bf52f07 100644
--- a/R/endpoints.R
+++ b/R/endpoints.R
@@ -10,8 +10,8 @@
 #' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from
 #' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models
 #'
-#' @param fit An object of class [mkinfit], [nlme.mmkin] or
-#'  [saem.mmkin]. Or another object that has list components
+#' @param fit An object of class [mkinfit], [nlme.mmkin], [saem.mmkin] or
+#'  [nlmixr.mmkin]. Or another object that has list components
 #'  mkinmod containing an [mkinmod] degradation model, and two numeric vectors,
 #'  bparms.optim and bparms.fixed, that contain parameter values
 #'  for that model.
diff --git a/R/mean_degparms.R b/R/mean_degparms.R
index ec7f4342..ec20c068 100644
--- a/R/mean_degparms.R
+++ b/R/mean_degparms.R
@@ -4,6 +4,7 @@
 #'   of the fitted degradation model parameters. If random is TRUE, a list with
 #'   fixed and random effects, in the format required by the start argument of
 #'   nlme for the case of a single grouping variable ds.
+#' @param object An mmkin row object containing several fits of the same model to different datasets
 #' @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
@@ -51,7 +52,7 @@ mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.l
 
     # For nlmixr we can specify starting values for standard deviations eta, and
     # we ignore uncertain parameters if test_log_parms is FALSE
-    eta <- apply(degparm_mat_trans_OK, 1, sd, na.rm = TRUE)
+    eta <- apply(degparm_mat_trans_OK, 1, stats::sd, na.rm = TRUE)
 
     return(list(fixed = fixed, random = list(ds = random), eta = eta))
   } else {
diff --git a/R/nlmixr.R b/R/nlmixr.R
index 223b23a1..98783ca7 100644
--- a/R/nlmixr.R
+++ b/R/nlmixr.R
@@ -1,4 +1,7 @@
-utils::globalVariables(c("predicted", "std"))
+utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES"))
+
+#' @export
+nlmixr::nlmixr
 
 #' Fit nonlinear mixed models using nlmixr
 #'
@@ -10,8 +13,10 @@ utils::globalVariables(c("predicted", "std"))
 #' obtained by fitting the same model to a list of datasets using [mkinfit].
 #'
 #' @importFrom nlmixr nlmixr tableControl
+#' @importFrom dplyr %>%
 #' @param object An [mmkin] row object containing several fits of the same
 #'   [mkinmod] model to different datasets
+#' @param data Not used, the data are extracted from the mmkin row object
 #' @param est Estimation method passed to [nlmixr::nlmixr]
 #' @param degparms_start Parameter values given as a named numeric vector will
 #'   be used to override the starting values obtained from the 'mmkin' object.
@@ -21,22 +26,28 @@ utils::globalVariables(c("predicted", "std"))
 #'   when calculating mean degradation parameters using [mean_degparms].
 #' @param conf.level Possibility to adjust the required confidence level
 #'   for parameter that are tested if requested by 'test_log_parms'.
-#' @param solution_type Possibility to specify the solution type in case the
-#'   automatic choice is not desired
-#' @param control Passed to [nlmixr::nlmixr].
+#' @param data Not used, as the data are extracted from the mmkin row object
+#' @param table Passed to [nlmixr::nlmixr]
+#' @param error_model Possibility to override the error model which is being
+#'   set based on the error model used in the mmkin row object.
+#' @param control Passed to [nlmixr::nlmixr]
 #' @param \dots Passed to [nlmixr_model]
+#' @param save Passed to [nlmixr::nlmixr]
+#' @param envir Passed to [nlmixr::nlmixr]
 #' @return An S3 object of class 'nlmixr.mmkin', containing the fitted
 #'   [nlmixr::nlmixr] object as a list component named 'nm'. The
 #'   object also inherits from 'mixed.mmkin'.
 #' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin]
 #' @examples
+#' \dontrun{
 #' ds <- lapply(experimental_data_for_UBA_2019[6:10],
 #'  function(x) subset(x$data[c("name", "time", "value")]))
 #' names(ds) <- paste("Dataset", 6:10)
+#' 
 #' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1)
 #' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc",
 #'   cores = 1, quiet = TRUE)
-#'
+#' 
 #' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem")
 #' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei")
 #'
@@ -66,7 +77,6 @@ utils::globalVariables(c("predicted", "std"))
 #' # solution, the two-component error model does not improve it
 #' plot(f_nlmixr_fomc_saem)
 #'
-#' \dontrun{
 #' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
 #'   A1 = mkinsub("SFO"))
 #' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
@@ -167,7 +177,8 @@ nlmixr.mmkin <- function(object, data = NULL,
   return_data <- nlmixr_df %>%
     dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV,
       predicted = IPRED, residual = IRES,
-      std = IRES/IWRES, standardized = IWRES)
+      std = IRES/IWRES, standardized = IWRES) %>%
+    dplyr::arrange(ds, name, time)
 
   bparms_optim <- backtransform_odeparms(f_nlmixr$theta,
     object[[1]]$mkinmod,
@@ -227,6 +238,9 @@ print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...)
 }
 
 #' @rdname nlmixr.mmkin
+#' @param add_attributes Should the starting values used for degradation model
+#'   parameters and their distribution and for the error model parameters
+#'   be returned as attributes?
 #' @return An function defining a model suitable for fitting with [nlmixr::nlmixr].
 #' @export
 nlmixr_model <- function(object,
@@ -435,6 +449,7 @@ nlmixr_model <- function(object,
 
   if (add_attributes) {
     attr(f, "mean_dp_start") <- degparms_optim
+    attr(f, "eta_start") <- degparms_mmkin$eta
     attr(f, "mean_ep_start") <- errparms_ini
   }
 
diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R
index ae8e32cf..f2d7c607 100644
--- a/R/summary.nlmixr.mmkin.R
+++ b/R/summary.nlmixr.mmkin.R
@@ -6,8 +6,9 @@
 #' endpoints such as formation fractions and DT50 values. Optionally
 #' (default is FALSE), the data are listed in full.
 #'
-#' @param object an object of class [nlmix.mmkin]
-#' @param x an object of class [summary.nlmix.mmkin]
+#' @importFrom stats confint sd
+#' @param object an object of class [nlmixr.mmkin]
+#' @param x an object of class [summary.nlmixr.mmkin]
 #' @param data logical, indicating whether the full data should be included in
 #'   the summary.
 #' @param verbose Should the summary be verbose?
@@ -23,9 +24,7 @@
 #'   \item{diffs}{The differential equations used in the degradation model}
 #'   \item{use_of_ff}{Was maximum or minimum use made of formation fractions}
 #'   \item{data}{The data}
-#'   \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals}
 #'   \item{confint_back}{Backtransformed parameters, with confidence intervals if available}
-#'   \item{confint_errmod}{Error model parameters with confidence intervals}
 #'   \item{ff}{The estimated formation fractions derived from the fitted
 #'      model.}
 #'   \item{distimes}{The DT50 and DT90 values for each observed variable.}
@@ -78,7 +77,7 @@
 #' # The following takes a very long time but gives
 #' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
 #' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
-#' summary(f_nlmixr_dfop_sfo, data = TRUE)
+#' summary(f_nlmixr_dfop_sfo_sfo, data = TRUE)
 #' }
 #'
 #' @export
@@ -134,6 +133,7 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes
     dim(varFix),
     list(pnames, pnames))
 
+  object$confint_trans <- confint_trans
   object$confint_back <- confint_back
 
   object$date.summary = date()
@@ -141,31 +141,29 @@ summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes
 
   object$diffs <- object$mkinmod$diffs
   object$print_data <- data # boolean: Should we print the data?
-  predict(object$nm)
-  so_pred <- object$so@results@predictions
 
   names(object$data)[4] <- "observed" # rename value to observed
 
   object$verbose <- verbose
 
   object$fixed <- object$mmkin_orig[[1]]$fixed
-  object$AIC = AIC(object$so)
-  object$BIC = BIC(object$so)
-  object$logLik = logLik(object$so, method = "is")
+  object$AIC = AIC(object$nm)
+  object$BIC = BIC(object$nm)
+  object$logLik = logLik(object$nm)
 
   ep <- endpoints(object)
   if (length(ep$ff) != 0)
     object$ff <- ep$ff
   if (distimes) object$distimes <- ep$distimes
   if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB
-  class(object) <- c("summary.saem.mmkin")
+  class(object) <- c("summary.nlmixr.mmkin")
   return(object)
 }
 
-#' @rdname summary.saem.mmkin
+#' @rdname summary.nlmixr.mmkin
 #' @export
-print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {
-  cat("saemix version used for fitting:     ", x$saemixversion, "\n")
+print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {
+  cat("nlmixr version used for fitting:   ", x$nlmixrversion, "\n")
   cat("mkin version used for pre-fitting: ", x$mkinversion, "\n")
   cat("R version used for fitting:        ", x$Rversion, "\n")
 
@@ -181,25 +179,29 @@ print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3)
     length(unique(x$data$name)), "variable(s) grouped in",
     length(unique(x$data$ds)), "datasets\n")
 
-  cat("\nModel predictions using solution type", x$solution_type, "\n")
+  cat("\nDegradation model predictions using RxODE\n")
 
-  cat("\nFitted in", x$time[["elapsed"]],  "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n")
+  cat("\nFitted in", x$time[["elapsed"]],  "s\n")
 
   cat("\nVariance model: ")
   cat(switch(x$err_mod,
     const = "Constant variance",
     obs = "Variance unique to each observed variable",
-    tc = "Two-component variance function"), "\n")
+    tc = "Two-component variance function",
+    obs_tc = "Two-component variance unique to each observed variable"), "\n")
 
   cat("\nMean of starting values for individual parameters:\n")
   print(x$mean_dp_start, digits = digits)
 
+  cat("\nMean of starting values for error model parameters:\n")
+  print(x$mean_ep_start, digits = digits)
+
   cat("\nFixed degradation parameter values:\n")
   if(length(x$fixed$value) == 0) cat("None\n")
   else print(x$fixed, digits = digits)
 
   cat("\nResults:\n\n")
-  cat("Likelihood computed by importance sampling\n")
+  cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \n")
   print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,
       row.names = " "), digits = digits)
 
@@ -212,16 +214,14 @@ print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3)
     print(corr, title = "\nCorrelation:", ...)
   }
 
-  cat("\nRandom effects:\n")
-  print(x$confint_ranef, digits = digits)
+  cat("\nRandom effects (omega):\n")
+  print(x$nm$omega, digits = digits)
 
   cat("\nVariance model:\n")
-  print(x$confint_errmod, digits = digits)
+  print(x$nm$sigma, digits = digits)
 
-  if (x$transformations == "mkin") {
-    cat("\nBacktransformed parameters:\n")
-    print(x$confint_back, digits = digits)
-  }
+  cat("\nBacktransformed parameters:\n")
+  print(x$confint_back, digits = digits)
 
   printSFORB <- !is.null(x$SFORB)
   if(printSFORB){