R: Partial Least Squares and Principal Components Regression

mvr {pls}

R Documentation

Partial Least Squares and Principal Components Regression

Description

Functions to perform partial least squares regression (PLSR) or principal components regression (PCR), with a formula interface. Cross-validation can be used. Prediction, model extraction, plot, print and summary methods exist.

Usage

mvr(formula, ncomp, data, subset, na.action,
    method = c("kernelpls", "simpls", "oscorespls", "svdpc"),
    validation = c("none", "CV", "LOO"),
    model = TRUE, x = FALSE, y = FALSE, ...)
plsr(..., method = c("kernelpls", "simpls", "oscorespls"))
pcr(..., method = "svdpc")

Arguments

`formula`	a model formula. Most of the `lm` formula constructs are supported.
`ncomp`	the number of components to include in the model (see below).
`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.
`method`	the multivariate regression method to be used.
`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`.

Details

The functions fit PLSR or PCR models with 1, ..., ncomp number of components. Multi-response models are fully supported.

Three PLSR algorithms are available: the kernel algorithm, SIMPLS and the classical orthogonal scores algorithm. One PCR algorithm is available: using the singular value decomposition. The type of regression is specified with the method argument. pcr and plsr are wrappers for mvr, with different values for method.

If validation = "CV", cross-validation is performed. The number and type of cross-validation segments are specified with the arguments segments and segment.type. See mvrCv for details. If validation = "LOO", leave-one-out cross-validation is performed. It is an error to specify the segments when validation = "LOO" is specified.

Note that the cross-validation is optimised for speed, and some generality has been sacrificed. Especially, the model matrix is calculated only once for the complete cross-validation, so models like y ~ msc(X) will not be properly cross-validated. For proper cross-validation of models where the model matrix must be updated/regenerated for each segment, use the separate function crossval.

Value

mvr returns an object of class mvr. The object contains all components returned by the underlying fit function. In addition, it contains the following components:

`validation`	if validation was requested, the results of the cross-validation. See `mvrCv` for details.
`ncomp`	the number of components of the model.
`method`	the method used to fit the model. See the argument `method` for possible values.
`call`	the function call.
`terms`	the model terms.
`model`	if `model = TRUE`, the model frame.
`x`	if `x = TRUE`, the model matrix.
`y`	if `y = TRUE`, the model response.

Author(s)

Ron Wehrens and Bjørn-Helge Mevik

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

Examples

data(NIR)
## Default methods:
NIR.pcr <- pcr(y ~ X, 6, data = NIR, CV = TRUE)
NIR.pls <- plsr(y ~ X, 6, data = NIR, CV = TRUE)

## Alternative methods:
NIR.oscorespls <- mvr(y ~ X, 6, data = NIR, CV = TRUE,
                      method = "oscorespls")
NIR.simpls <- mvr(y ~ X, 6, data = NIR, CV = TRUE,
                  method = "simpls")

data(sensory)
Pn <- scale(sensory$Panel)
Ql <- scale(sensory$Quality)
sens.pcr <- pcr(Ql ~ Pn, ncomp = 4)
sens.pls <- plsr(Ql ~ Pn, ncomp = 4)

[Package pls version 1.0-2 Index]