pls (version 2.8-4)

predict.mvr: 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 mvr
predict(
  object,
  newdata,
  ncomp = 1:object$ncomp,
  comps,
  type = c("response", "scores"),
  na.action = na.pass,
  ...
)

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.

Arguments

object

an mvr object. The fitted model

newdata

a data frame. The new data. If missing, the training data is used.

ncomp, comps

vector of positive integers. The components to use in the prediction. See below.

type

character. Whether to predict scores or response values

na.action

function determining what should be done with missing values in newdata. The default is to predict NA. See na.omit for alternatives.

...

further arguments. Currently not used

Author

Ron Wehrens and Bjørn-Helge Mevik

Details

When type is "response" (default), predicted response values are returned. If comps is missing (or is NULL), predictions for length(ncomp) models with ncomp[1] components, ncomp[2] components, etc., are returned. Otherwise, predictions for a single model with the exact components in comps are returned. (Note that in both cases, the intercept is always included in the predictions. It can be removed by subtracting the Ymeans component of the fitted model.)

When type is "scores", predicted score values are returned for the components given in comps. If comps is missing or NULL, ncomps is used instead.

It is also possible to supply a matrix instead of a data frame as newdata, which is then assumed to be the \(X\) data matrix. Note that the usual checks for the type of the data are then omitted. Also note that this is only possible with predict; it will not work in functions like predplot, RMSEP or R2, because they also need the response variable of the new data.

See Also

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

Examples

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

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

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

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