mvr

From pls v1.0-1 by Bj�rn-Helge Mevik

Partial Least Squares and Principal Components Regression

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.

Keywords: multivariate, regression

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")

Details

The functions fit PLSR or PCR models with 1, $\ldots$, 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:
validationif validation was requested, the results of the cross-validation. See mvrCv for details.
ncompthe number of components of the model.
methodthe method used to fit the model. See the argument method for possible values.
callthe function call.
termsthe model terms.
modelif model = TRUE, the model frame.
xif x = TRUE, the model matrix.
yif y = TRUE, the model response.

References

Martens, H., N�s, 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)

Documentation reproduced from package pls, version 1.0-1, License: GPL version 2

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mvr