From a1631098acfc3352e19c331e568bd6f5766b3c3d Mon Sep 17 00:00:00 2001
From: Johannes Ranke  Not intended to be called directly by the user.plot_mixed(
+  x,
+  i = 1:ncol(x$mmkin),
+  degparms_optim,
+  degparms_pop,
+  residuals,
+  obs_vars = names(x$mkinmod$map),
+  standardized = TRUE,
+  xlab = "Time",
+  xlim = range(x$data$time),
+  resplot = c("predicted", "time"),
+  ymax = "auto",
+  maxabs = "auto",
+  ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),
+  nrow.legend = ceiling((length(i) + 1)/ncol.legend),
+  rel.height.legend = 0.03 + 0.08 * nrow.legend,
+  rel.height.bottom = 1.1,
+  pch_ds = 1:length(i),
+  col_ds = pch_ds + 1,
+  lty_ds = col_ds,
+  frame = TRUE,
+  ...
+)
+
+
+
+  
Plot a fitted nonlinear mixed model obtained via an mmkin row object
Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
Summary method for class "saem.mmkin"
Plot an saem fitted nonlinear mixed model obtained via an mmkin row object
R/plot_mixed.R
+    plot_mixed.RdPlot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
+# S3 method for saem.mmkin +plot( + x, + i = 1:ncol(x$mmkin), + obs_vars = names(x$mkinmod$map), + standardized = TRUE, + xlab = "Time", + xlim = range(x$data$time), + resplot = c("predicted", "time"), + ymax = "auto", + maxabs = "auto", + ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), + nrow.legend = ceiling((length(i) + 1)/ncol.legend), + rel.height.legend = 0.03 + 0.08 * nrow.legend, + rel.height.bottom = 1.1, + pch_ds = 1:length(i), + col_ds = pch_ds + 1, + lty_ds = col_ds, + frame = TRUE, + ... +) + +# S3 method for nlme.mmkin +plot( + x, + i = 1:ncol(x$mmkin), + obs_vars = names(x$mkinmod$map), + standardized = TRUE, + xlab = "Time", + xlim = range(x$data$time), + resplot = c("predicted", "time"), + ymax = "auto", + maxabs = "auto", + ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), + nrow.legend = ceiling((length(i) + 1)/ncol.legend), + rel.height.legend = 0.03 + 0.08 * nrow.legend, + rel.height.bottom = 1.1, + pch_ds = 1:length(i), + col_ds = pch_ds + 1, + lty_ds = col_ds, + frame = TRUE, + ... +)+ +
| x | +An object of class saem.mmkin or nlme.mmkin  | 
+    
|---|---|
| i | +A numeric index to select datasets for which to plot the individual predictions, +in case plots get too large  | 
+    
| obs_vars | +A character vector of names of the observed variables for +which the data and the model should be plotted. Defauls to all observed +variables in the model.  | 
+    
| standardized | +Should the residuals be standardized? Only takes effect if
+  | 
+    
| xlab | +Label for the x axis.  | 
+    
| xlim | +Plot range in x direction.  | 
+    
| resplot | +Should the residuals plotted against time or against +predicted values?  | 
+    
| ymax | +Vector of maximum y axis values  | 
+    
| maxabs | +Maximum absolute value of the residuals. This is used for the +scaling of the y axis and defaults to "auto".  | 
+    
| ncol.legend | +Number of columns to use in the legend  | 
+    
| nrow.legend | +Number of rows to use in the legend  | 
+    
| rel.height.legend | +The relative height of the legend shown on top  | 
+    
| rel.height.bottom | +The relative height of the bottom plot row  | 
+    
| pch_ds | +Symbols to be used for plotting the data.  | 
+    
| col_ds | +Colors used for plotting the observed data and the +corresponding model prediction lines for the different datasets.  | 
+    
| lty_ds | +Line types to be used for the model predictions.  | 
+    
| frame | +Should a frame be drawn around the plots?  | 
+    
| ... | +Further arguments passed to   | 
+    
The functions are called for their side effect.
+Johannes Ranke
+ ++ds <- lapply(experimental_data_for_UBA_2019[6:10], + function(x) x$data[c("name", "time", "value")]) +names(ds) <- paste0("ds ", 6:10) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO"), quiet = TRUE) +# \dontrun{ +f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE) +plot(f[, 3:4], standardized = TRUE) ++library(nlme) +# For this fit we need to increase pnlsMaxiter, and we increase the +# tolerance in order to speed up the fit for this example evaluation +f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) +plot(f_nlme) +#> Running main SAEM algorithm +#> [1] "Mon Nov 9 16:07:05 2020" +#> .... +#> Minimisation finished +#> [1] "Mon Nov 9 16:07:14 2020"# } +