predict.mvr {pls}R Documentation

Predict Method for PLSR and PCR

Description

Prediction for MVR (PCR, PLSR) models. New responses or scores are predicted using a fitted model and a new matrix of observations.

Usage

## S3 method for class 'mvr':
predict(object, newdata, comps = 1:object$ncomp,
        type = c("response", "scores"), cumulative = TRUE, ...)

Arguments

object an mvr object. The fitted model
newdata a data frame. The new data. If missing, the training data is used.
comps vector of positive integers. The components to use in the prediction. See below.
type character. Whether to predict scores or response values
cumulative logical. How the elements of comps are interpreted. Ignored if type is "scores". See below
... further arguments. Currently not used

Details

When type is "response" (default), predicted response values are returned. If cumulative is TRUE, the elements of comps are interpreted cumulatively, i.e. predictions for models with comps[1] components, comps[2] components, etc., are returned. Otherwise, predicted response values for a single model with the exact components in comps are returned.

When type is "scores", predicted score values are returned for the components given in comps.

Value

When type is "response", a three dimensional array of predicted response values is returned. The dimensions correspond to the observations, the response variables and the model sizes, respectively.
When type is "scores", a score matrix is returned.

Author(s)

Ron Wehnrens and Bjørn-Helge Mevik

See Also

mvr, summary.mvr, coef.mvr, plot.mvr

Examples

data(NIR)
nir.mvr <- mvr(y ~ X, ncomp = 5, data = NIR[NIR$train,])

## Predicted responses for models with 1, 2, 3 and 4 components
pred.resp <- predict(nir.mvr, comps = 1:4, newdata = NIR[!NIR$train,])

## Predicted responses for a single model with components 1, 2, 3, 4
predict(nir.mvr, comps = 1:4, cumulative = FALSE, newdata = NIR[!NIR$train,])

## Predicted scores
predict(nir.mvr, comps = 1:3, type = "scores", newdata = NIR[!NIR$train,])

[Package pls version 1.0-2 Index]