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

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# 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) <= 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,
-  ...
-)

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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

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The function is called for its side effect.

Note

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

Author

Johannes Ranke

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Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) x$data[c("name", "time", "value")])
-names(ds) <- paste0("ds ", 6:10)
-dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-  A1 = mkinsub("SFO"), quiet = TRUE)
-# \dontrun{
-f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
-plot(f[, 3:4], standardized = TRUE)
-
-
-# For this fit we need to increase pnlsMaxiter, and we increase the
-# tolerance in order to speed up the fit for this example evaluation
-# It still takes 20 seconds to run
-f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
-plot(f_nlme)
-
-
-f_saem <- saem(f, transformations = "saemix")
-plot(f_saem)
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-f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs")
-f_nlmix <- nlmix(f_obs)
-#> Error in nlmix(f_obs): could not find function "nlmix"
-plot(f_nlmix)
-#> Error in plot(f_nlmix): object 'f_nlmix' not found
-
-# We can overlay the two variants if we generate predictions
-pred_nlme <- mkinpredict(dfop_sfo,
-  f_nlme$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))
-
-# }
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