predict.spls: Make predictions or extract coefficients from a fitted SPLS...

In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

Make predictions or extract coefficients from a fitted SPLS model

Description

Make predictions or extract coefficients from a fitted SPLS object.

Usage

## S3 method for class 'spls'
predict( object, newx, type = c("fit","coefficient"), ... )
## S3 method for class 'spls'
coef( object, ... )

Arguments

object	A fitted SPLS object.
newx	If type="fit", then newx should be the predictor matrix of test dataset. If newx is omitted, then prediction of training dataset is returned. If type="coefficient", then newx can be omitted.
type	If type="fit", fitted values are returned. If type="coefficient", coefficient estimates of SPLS fits are returned.
...	Any arguments for predict.spls should work for coef.spls.

Details

Users can input either only selected variables or all variables for newx.

Value

Matrix of coefficient estimates if type="coefficient". Matrix of predicted responses if type="fit".

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

plot.spls and print.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 latent components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
# Coefficient estimates of the SPLS fit
coef.f <- coef(f)
coef.f[1:5,]
# Prediction on the training dataset
pred.f <- predict( f, type="fit" )
pred.f[1:5,]

spls documentation built on June 30, 2025, 9:07 a.m.