plsr.mcSimulation {decisionSupport}

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

Description

Perform a Partial Least Squares Regression (PLSR) of Monte Carlo simulation results.

Usage

plsr.mcSimulation(
  object,
  resultName = NULL,
  variables.x = names(object$x),
  method = "oscorespls",
  scale = TRUE,
  ncomp = 2,
  ...
)

Arguments

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.

Value

An object of class mvr.

See Also

mcSimulation, plsr, summary.mvr, biplot.mvr, coef.mvr, plot.mvr,