plsr.mcSimulation: Partial Least Squares Regression (PLSR) of Monte Carlo simulation results.

Description

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 mvr.

object: An object of class mcSimulation.
resultName: 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.
variables.x: 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.
method: the multivariate regression method to be used. If "model.frame", the model frame is returned.
scale: 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.
ncomp: the number of components to include in the model (see below).
...: further arguments to be passed to plsr.