predict.pcr: Predicted values from a principal components regression
In gavinsimpson/analogue: Analogue and Weighted Averaging Methods for Palaeoecology

Description Usage Arguments Details Value Author(s) See Also Examples

Calculates predicted values from a fitted principal components regression model. Leave-one-out, bootstrap or n k-fold crossvalidated predictions are also implemented.

## S3 method for class 'pcr'
predict(object, newdata, ncomp = object$ncomp,
        CV = c("none", "LOO", "bootstrap", "kfold"),
        verbose = FALSE, nboot = 100, kfold = 10, folds = 5,
        ...)

`object`	a fitted model of class `"pcr"`, the result of a call to `pcr`.
`newdata`	data frame of new observations for which predictions are sought.
`ncomp`	numeric; the PCR components for which predictions are sought. If `ncomp = c`, predictions for components `1:c` are produced.
`CV`	character; the type of crossvalidation required. Currently, no crossvalidation methods are implemented.
`verbose`	logical; should progress on crossvalidation be printed to the console?
`nboot`	numeric; the number of bootstrap samples to draw.
`kfold`	numeric; the number of folds to split data into.
`folds`	numeric; the number of repetitions of k-fold CV.
`...`	arguments passed to other methods.

predict.pcr arranges for any transformation applied to the training data to be applied to the newdata prior to prediction.

A matrix of predicted values with rows representing samples in newdata and columns, the PCR components requested via ncomp.

Gavin L. Simpson

pcr

## Load the Imbrie & Kipp data and
## summer sea-surface temperatures
data(ImbrieKipp)
data(SumSST)

## choose 10 samples to act as a test set, for illustration
take <- c(5,58,31,51,42,28,30,57,8,50)

## normal interface and apply Hellinger transformation
mod <- pcr(ImbrieKipp[-take, ], SumSST[-take], tranFun = Hellinger)

## predictions
predict(mod, ImbrieKipp[take, ], ncomp = 4)

## predictions
set.seed(123)
predict(mod, ImbrieKipp[take, ], ncomp = 4, CV = "bootstrap",
        nboot = 100)