From 47ba9ea512b82fb8b31da8ec5558f3c0952d86d4 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Thu, 2 Apr 2020 10:58:34 +0200
Subject: Compiled models article, reduce distractions

- Added a section with platform specific notes on getting compiled
models to work to the compiled models article
- Don't return empty SFORB parameter list from endpoints() if there is no
SFORB model
- Avoid warnings when using standardized = TRUE in plot.mmkin()
---
 R/endpoints.R  | 20 +++++++++++---------
 R/plot.mmkin.R |  6 +++++-
 2 files changed, 16 insertions(+), 10 deletions(-)

(limited to 'R')

diff --git a/R/endpoints.R b/R/endpoints.R
index 14beadea..f7ee483a 100644
--- a/R/endpoints.R
+++ b/R/endpoints.R
@@ -1,12 +1,12 @@
 #' Function to calculate endpoints for further use from kinetic models fitted
 #' with mkinfit
-#' 
+#'
 #' This function calculates DT50 and DT90 values as well as formation fractions
 #' from kinetic models fitted with mkinfit. If the SFORB model was specified
 #' for one of the parents or metabolites, the Eigenvalues are returned. These
 #' are equivalent to the rate constantes of the DFOP model, but with the
 #' advantage that the SFORB model can also be used for metabolites.
-#' 
+#'
 #' @param fit An object of class \code{\link{mkinfit}}.
 #' @importFrom stats optimize
 #' @return A list with the components mentioned above.
@@ -14,10 +14,10 @@
 #' @author Johannes Ranke
 #' @keywords manip
 #' @examples
-#' 
+#'
 #'   fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-#'   endpoints(fit)  
-#' 
+#'   endpoints(fit)
+#'
 #' @export
 endpoints <- function(fit) {
   # Calculate dissipation times DT50 and DT90 and formation
@@ -29,8 +29,9 @@ endpoints <- function(fit) {
   parms.all <- c(fit$bparms.optim, fit$bparms.fixed)
   ep$ff <- vector()
   ep$SFORB <- vector()
-  ep$distimes <- data.frame(DT50 = rep(NA, length(obs_vars)),
-			    DT90 = rep(NA, length(obs_vars)),
+  ep$distimes <- data.frame(
+    DT50 = rep(NA, length(obs_vars)),
+    DT90 = rep(NA, length(obs_vars)),
     row.names = obs_vars)
   for (obs_var in obs_vars) {
     type = names(fit$mkinmod$map[[obs_var]])[1]
@@ -41,8 +42,8 @@ endpoints <- function(fit) {
       f_values = parms.all[f_names]
       f_to_sink = 1 - sum(f_values)
       names(f_to_sink) = ifelse(type == "SFORB",
-                              paste(obs_var, "free", "sink", sep = "_"),
-                              paste(obs_var, "sink", sep = "_"))
+        paste(obs_var, "free", "sink", sep = "_"),
+        paste(obs_var, "sink", sep = "_"))
       for (f_name in f_names) {
         ep$ff[[sub("f_", "", sub("_to_", "_", f_name))]] = f_values[[f_name]]
       }
@@ -195,5 +196,6 @@ endpoints <- function(fit) {
     }
     ep$distimes[obs_var, c("DT50", "DT90")] = c(DT50, DT90)
   }
+  if (length(ep$SFORB) == 0) ep$SFORB <- NULL
   return(ep)
 }
diff --git a/R/plot.mmkin.R b/R/plot.mmkin.R
index 182e74ca..15ea86eb 100644
--- a/R/plot.mmkin.R
+++ b/R/plot.mmkin.R
@@ -15,6 +15,9 @@
 #' @param resplot Should the residuals plotted against time, using
 #'   \code{\link{mkinresplot}}, or as squared residuals against predicted
 #'   values, with the error model, using \code{\link{mkinerrplot}}.
+#' @param standardized Should the residuals be standardized? This option
+#'   is passed to \code{\link{mkinresplot}}, it only takes effect if 
+#'   `resplot = "time"`.
 #' @param show_errmin Should the chi2 error level be shown on top of the plots
 #'   to the left?
 #' @param errmin_var The variable for which the FOCUS chi2 error value should
@@ -51,6 +54,7 @@
 #' @export
 plot.mmkin <- function(x, main = "auto", legends = 1,
                        resplot = c("time", "errmod"),
+                       standardized = FALSE,
                        show_errmin = TRUE,
                        errmin_var = "All data", errmin_digits = 3,
                        cex = 0.7, rel.height.middle = 0.9,
@@ -136,7 +140,7 @@ plot.mmkin <- function(x, main = "auto", legends = 1,
     }
 
     if (resplot == "time") {
-      mkinresplot(fit, legend = FALSE, ...)
+      mkinresplot(fit, legend = FALSE, standardized = standardized, ...)
     } else {
       mkinerrplot(fit, legend = FALSE, ...)
     }
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