predict.spls: Make predictions or extract coefficients from a fitted SPLS...
In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

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

Make predictions or extract coefficients from a fitted SPLS object.

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

`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`.

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

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

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

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.

plot.spls and print.spls.

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,]