R/plot.mixed.mmkin.R
plot.mixed.mmkin.Rd
Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
# S3 method for mixed.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"), pred_over = NULL, 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 = 0.02 + 0.07 * 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 mixed.mmkin, 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? 
pred_over  Named list of alternative predictions as obtained from mkinpredict with a compatible mkinmod. 
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 function is called for its 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("DFOPSFO" = dfop_sfo), ds, quiet = TRUE) plot(f[, 3:4], standardized = TRUE)# 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 = 1e3)) plot(f_nlme)# }