plsr.mcSimulation: Partial Least Squares Regression (PLSR) of Monte Carlo...
In decisionSupport: Quantitative Support of Decision Making under Uncertainty

plsr.mcSimulation

R Documentation

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

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,
  ...
)

`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`.

An object of class mvr.