Make predictions or extract coefficients from a fitted SGPLS model
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 |
If |
fit.type |
If |
... |
Any arguments for |
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 (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.
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" ))
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