R: Model predictions based on a fitted "grpsurv" object.

predict.grpsurv {grpreg} | R Documentation

## Model predictions based on a fitted "grpsurv" object.

### Description

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

### Usage

    ## S3 method for class 'grpsurv'
    predict(object, X, type=c("link", "response", "survival",
    "median", "norm", "coefficients", "vars", "nvars", "groups", "ngroups"),
    lambda, which=1:length(object$lambda), ...)

### Arguments

`object` | Fitted `"grpsurv"` model object.
`X` | Matrix of values at which predictions are to be made. Not used for `type="coefficients"` or for some of the `type` settings in `predict`.
`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 risk (i.e., exp(link)); `"survival"` returns the estimated survival function; `"median"` estimates median survival times. The other options are all identical to their `grpreg` counterparts: `"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.
`...` | Not used.

### Details

Estimation of baseline survival function conditional on the estimated values of `beta` is carried out according to the method described in Chapter 4.3 of Kalbfleish and Prentice. In particular, it agrees exactly with the results returned by `survfit.coxph(..., type='kalbfleisch-prentice')` in the `survival` package.

### Value

The object returned depends on type.

### Author(s)

Patrick Breheny

### References

- Kalbfleish JD and Prentice RL (2002). The Statistical Analysis of Failure Time Data, 2nd edition. Wiley.

### See Also

`grpsurv`

### Examples

    data(Lung)
    X <- Lung$X
    y <- Lung$y
    group <- Lung$group

    fit <- grpsurv(X, y, group)
    coef(fit, lambda=0.05)
    head(predict(fit, X, type="link", lambda=0.05))
    head(predict(fit, X, type="response", lambda=0.05))

    # Survival function
    S <- predict(fit, X[1,], type="survival", lambda=0.05)
    S(100)
    S <- predict(fit, X, type="survival", lambda=0.05)
    plot(S, xlim=c(0,200))

    # Medians
    predict(fit, X[1,], type="median", lambda=0.05)
    M <- predict(fit, X, type="median")
    M[1:10, 1:10]

    # Nonzero coefficients
    predict(fit, type="vars", lambda=c(0.1, 0.01))
    predict(fit, type="nvars", lambda=c(0.1, 0.01))

[Package grpreg version 3.2-1 Index]