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))
+
+# }
+