R: Model predictions for grpsurv objects

predict.grpsurv {grpreg} | R Documentation

## Model predictions for grpsurv objects

### 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", "hazard", "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 required for some `type` values.
`type` | Type of prediction: `link`: linear predictors `response`: risk (i.e., `exp(link)`) `survival`: the estimated survival function `hazard`: the estimated cumulative hazard function `median`: median survival time The other options are all identical to their `grpreg()` counterparts
`lambda` | Regularization parameter 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. Default: all indices. If `lambda` is specified, this will override `which`.
`...` | 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 Kalbfleisch and Prentice.

### 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 3.4.0 Index]