Perform a Partial Least Squares Regression (PLSR) of Monte Carlo simulation results.
plsr.mcSimulation(object, resultName = NULL,
variables.x = names(object$x), method = "oscorespls", scale = TRUE,
ncomp = 2, ...)An object of class mcSimulation.
character: indicating the name of the component of
the simulation function (model_function) whose results histogram
shall be generated. If model_function is single valued, no name
needs to be supplied. Otherwise, one valid name has to be specified.
Defaults to NULL.
character or character vector: Names of the
components of the input variables to the simulation function, i.e. the
names of the variables in the input estimate which random sampling
results shall be displayed. Defaults to all components.
the multivariate regression method to be used. If
"model.frame", the model frame is returned.
numeric vector, or logical. If numeric vector, \(X\)
is scaled by dividing each variable with the corresponding element
of scale. If scale is TRUE, \(X\) is scaled
by dividing each variable by its sample standard deviation. If
cross-validation is selected, scaling by the standard deviation is
done for every segment.
the number of components to include in the model (see below).
further arguments to be passed to plsr.
An object of class mvr.
mcSimulation, plsr,
summary.mvr, biplot.mvr,
coef.mvr, plot.mvr,