| predict.spls {spls} | R Documentation |
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 the prediction of training dataset is returned.
If type="coefficient", then newx can be omitted.
|
type |
If type="fit", the fitted values are returned.
If type="coefficient",
the coefficient estimates of SPLS fits are returned.
|
... |
Any arguments for predict.spls
should work for coef.spls. |
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. (2007). "Sparse partial least squares for simultaneous dimension reduction and variable selection", (http://www.stat.wisc.edu/~keles/Papers/SPLS_Nov07.pdf).
plot and print methods for 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,]