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

Usage

# S3 method for class '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) <= 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 = c(1:25, 33, 35:38, 40:41, 47:57, 60:90)[1:length(i)],
  col_ds = pch_ds + 1,
  lty_ds = col_ds,
  frame = TRUE,
  ...
)

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.

Value

The function is called for its side effect.

Note

Covariate models are currently only supported for saem.mmkin objects.

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)
# \dontrun{
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)
#> Error in nlmix(f_obs): could not find function "nlmix"
plot(f_nlmix)
#> Error: object 'f_nlmix' not found

# 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))

# }