R: Make predictions or extract coefficients from a fitted SPLSDA...

predict.splsda {spls} | R Documentation

## Make predictions or extract coefficients from a fitted SPLSDA model

### Description

Make predictions or extract coefficients from a fitted SPLSDA object.

### Usage

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

### Arguments

`object` | A fitted SPLSDA 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 SPLSDA 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.splsda` should work for `coef.splsda`.

### 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"` (responses will be predicted classes if `fit.type="class"` or predicted probabilities if `fit.type="response"`).

### Author(s)

Dongjun Chung and Sunduz Keles.

### References

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.

### See Also

`print.splsda`.

### Examples

    data(prostate)
    # SPLSDA with eta=0.8 & 3 hidden components
    f <- splsda( prostate$x, prostate$y, K=3, eta=0.8, 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" ))

[Package spls version 2.2-2 Index]