Multivariate regression function

Description

Adaptation of mvr from package pls v 2.4.3.

Usage

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mvrV(formula, ncomp, Y.add, data, subset, na.action, shrink,
  method = c("truncation", "stpls", "model.frame"), scale = FALSE,
  validation = c("none", "CV", "LOO"), model = TRUE, x = FALSE,
  y = FALSE, ...)

Arguments

formula

a model formula. Most of the lm formula constructs are supported. See below.

ncomp

the number of components to include in the model (see below).

Y.add

a vector or matrix of additional responses containing relevant information about the observations. Only used for cppls.

data

an optional data frame with the data to fit the model from.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain missing values. The default is set by the na.action setting of options, and is na.fail if that is unset. The 'factory-fresh' default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful. See na.omit for other alternatives.

shrink

optional shrinkage parameter for stpls.

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.

validation

character. What kind of (internal) validation to use. See below.

model

a logical. If TRUE, the model frame is returned.

x

a logical. If TRUE, the model matrix is returned.

y

a logical. If TRUE, the response is returned.

...

additional arguments, passed to the underlying fit functions, and mvrCv.

See Also

mvr