predict.widenet


The <code>"widenet"</code> object from which to get predictions.


object


Matrix of new values for <code>x</code> at which predictions are to be made. Must be a matrix; sparse matrices are not yet supported. This argument is not used for <code>type=c("coefficients","nonzero")</code>


newx


Specifies the order for which predictions are required. Must be equal to one of the elements of the <code>order</code> argument for the <code>object</code>. The default is to use that order which "won" the cross-validation.


order


Value of <code>alpha</code> at which predictions are required. Default is to use that value which "won" the cross-validation.


alpha.val


See <code>link[glmnet]{predict.glmnet}</code>.


type


Further arguments passed to <code>predict.cv.relaxnet</code>, which should be passed through to <code>predict.glmnet</code>. However, in the current version, these are not guaranteed to work correctly (for example, <code>offset</code> has not yet been implemented for widenet).



Similar to other predict methods, this functions predicts fitted values, logits,
coefficients and more from a fitted <code>"widenet"</code> object.


Extends the glmnet and relaxnet packages with polynomial basis expansions. Basis expansion is applied to the predictors and a subset of the basis functions is chosen using relaxnet. Predictors may be screened using correlation or t-tests. Screening is done separately within cross-validation folds. Cross-validation may be used to select the order of basis expansion and alpha, the elastic net tuning parameter.

predict.widenet: Predict Method for `"widenet"` Objects

Description

Usage

Arguments

Value

See Also