predict.sgpls {spls} | R Documentation |
Make predictions or extract coefficients from a fitted SGPLS object.
## S3 method for class 'sgpls': predict( object, newx, type = c("fit","coefficient"), fit.type = c("class","response"), ... ) ## S3 method for class 'sgpls': coef( object, ... )
object |
A fitted SGPLS 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 SGPLS fits are returned.
|
fit.type |
If fit.type="class" , fitted classes are returned.
If fit.type="response" , fitted probabilities are returned.
Relevant only when type="fit" .
|
... |
Any arguments for predict.sgpls
should work for coef.sgpls . |
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"
(responses will be predicted classes if fit.type="class"
or predicted probabilities if fit.type="response"
).
Dongjun Chung and Sunduz Keles.
Chung, D. and Keles, S. (2009). "Sparse partial least squares classification for high dimensional data" (http://www.stat.wisc.edu/~keles/Papers/C_SPLS.pdf).
data(prostate) # SGPLS with eta=0.55 & 3 hidden components f <- sgpls( prostate$x, prostate$y, K=3, eta=0.55, scale.x=FALSE ) # Print out coefficients coef.f <- coef(f) coef.f[ coef.f!=0, ] # Prediction on the training dataset (pred.f <- predict( f, type="fit" ))