R: Model predictions based on a fitted 'grpreg' object

predict.grpreg {grpreg} | R Documentation

## Model predictions based on a fitted `grpreg` object

### Description

Similar to other predict methods, this function returns predictions from a fitted `"grpreg"` object.

### Usage

    ## S3 method for class 'grpreg'
    predict(object, X, type=c("link", "response", "class",
    "coefficients", "vars", "groups", "nvars", "ngroups", "norm"), lambda,
    which=1:length(object$lambda), ...)
    ## S3 method for class 'grpreg'
    coef(object, lambda, which=1:length(object$lambda),
    drop=TRUE, ...)
    ## S3 method for class 'cv.grpreg'
    predict(object, X, lambda=object$lambda.min,
    which=object$min, type=c("link", "response", "class", "coefficients",
    "vars", "groups", "nvars", "ngroups", "norm"), ...)
    ## S3 method for class 'cv.grpreg'
    coef(object, lambda=object$lambda.min,
    which=object$min, ...)

### Arguments

`object` | Fitted `"grpreg"` or `"cv.grpreg"` model object.
`X` | Matrix of values at which predictions are to be made. Not used for `type="coefficients"`
`lambda` | Values of the regularization parameter `lambda` at which predictions are requested. For values of `lambda` not in the sequence of fitted models, linear interpolation is used.
`which` | Indices of the penalty parameter `lambda` at which predictions are required. By default, all indices are returned. If `lambda` is specified, this will override `which`.
`type` | Type of prediction: `"link"` returns the linear predictors; `"response"` gives the fitted values; `"class"` returns the binomial outcome with the highest probability; `"coefficients"` returns the coefficients; `"vars"` returns the indices for the nonzero coefficients; `"groups"` returns the indices for the groups with at least one nonzero coefficient; `"nvars"` returns the number of nonzero coefficients; `"ngroups"` returns the number of groups with at least one nonzero coefficient; `"norm"` returns the L2 norm of the coefficients in each group.
`drop` | By default, if a single value of `lambda` is supplied, a vector of coefficients is returned. Set `drop=FALSE` if you wish to have `coef` always return a matrix (see `drop`).
`...` | Not used.

### Details

`coef` and `predict` methods are provided for `"cv.grpreg"` options as a convenience. They simply call `coef.grpreg` and `predict.grpreg` with `lambda` set to the value that minimizes the cross-validation error.

### Value

The object returned depends on type.

### Author(s)

Patrick Breheny <patrick-breheny@uiowa.edu>

### See Also

`grpreg`

### Examples

    # Fit penalized logistic regression model to birthweight data
    data(Birthwt)
    X <- Birthwt$X
    y <- Birthwt$low
    group <- Birthwt$group
    fit <- grpreg(X, y, group, penalty="grLasso", family="binomial")

    # Coef and predict methods
    coef(fit, lambda=.001)
    predict(fit, X, type="link", lambda=.001)
    predict(fit, X, type="response", lambda=.001)
    predict(fit, X, type="class", lambda=.001)
    predict(fit, type="vars", lambda=.07)
    predict(fit, type="groups", lambda=.07)
    predict(fit, type="norm", lambda=.07)

    # Coef and predict methods for cross-validation
    cvfit <- cv.grpreg(X, y, group, family="binomial", penalty="grMCP")
    coef(cvfit)
    predict(cvfit, X)
    predict(cvfit, X, type="response")
    predict(cvfit, type="groups")

[Package grpreg version 3.0-2 Index]