mkin (version 1.2.6)

plot.mixed.mmkin: Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object

Description

Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object

Usage

# S3 method for mixed.mmkin
plot(
  x,
  i = 1:ncol(x$mmkin),
  obs_vars = names(x$mkinmod$map),
  standardized = TRUE,
  covariates = NULL,
  covariate_quantiles = c(0.5, 0.05, 0.95),
  xlab = "Time",
  xlim = range(x$data$time),
  resplot = c("predicted", "time"),
  pop_curves = "auto",
  pred_over = NULL,
  test_log_parms = FALSE,
  conf.level = 0.6,
  default_log_parms = NA,
  ymax = "auto",
  maxabs = "auto",
  ncol.legend = ifelse(length(i) 

Value

The function is called for its side effect.

Arguments

x

An object of class mixed.mmkin, 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 resplot = "time".

covariates

Data frame with covariate values for all variables in any covariate models in the object. If given, it overrides 'covariate_quantiles'. Each line in the data frame will result in a line drawn for the population. Rownames are used in the legend to label the lines.

covariate_quantiles

This argument only has an effect if the fitted object has covariate models. If so, the default is to show three population curves, for the 5th percentile, the 50th percentile and the 95th percentile of the covariate values used for fitting the model.

xlab

Label for the x axis.

xlim

Plot range in x direction.

resplot

Should the residuals plotted against time or against predicted values?

pop_curves

Per default, one population curve is drawn in case population parameters are fitted by the model, e.g. for saem objects. In case there is a covariate model, the behaviour depends on the value of 'covariates'

pred_over

Named list of alternative predictions as obtained from mkinpredict with a compatible mkinmod.

test_log_parms

Passed to mean_degparms in the case of an mixed.mmkin object

conf.level

Passed to mean_degparms in the case of an mixed.mmkin object

default_log_parms

Passed to mean_degparms in the case of an mixed.mmkin object

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 plot.

Author

Johannes Ranke

Examples

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)
if (FALSE) {
f <- mmkin(list("DFOP-SFO" = 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
# It still takes 20 seconds to run
f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
plot(f_nlme)

f_saem <- saem(f, transformations = "saemix")
plot(f_saem)

f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs")
f_nlmix <- nlmix(f_obs)
plot(f_nlmix)

# We can overlay the two variants if we generate predictions
pred_nlme <- mkinpredict(dfop_sfo,
  f_nlme$bparms.optim[-1],
  c(parent = f_nlme$bparms.optim[[1]], A1 = 0),
  seq(0, 180, by = 0.2))
plot(f_saem, pred_over = list(nlme = pred_nlme))
}