predict.survreg {survival} | R Documentation

## Predicted Values for a 'survreg' Object

### Description

Predicted values for a `survreg` object

### Usage

    ## S3 method for class 'survreg':
    predict(object, newdata,
    type=c("response", "link", "lp", "linear",  "terms", "quantile",
            "uquantile"),
    se.fit=FALSE, terms=NULL, p=c(0.1, 0.9),...)

### Arguments

`object` | result of a model fit using the `survreg` function.
`newdata` | data for prediction. If absent, predictions are for the subjects used in the original fit.
`type` | the type of predicted value. This can be on the original scale of the data (response), the linear predictor (`"linear"`, with `"lp"` as an allowed abbreviation), a predicted quantile on the original scale of the data (`"quantile"`), a quantile on the linear predictor scale (`"uquantile"`), or the matrix of terms for the linear predictor (`"terms"`). At this time `"link"` and linear predictor (`"lp"`) are identical.
`se.fit` | if TRUE, include the standard errors of the prediction in the result.
`terms` | subset of terms. The default for residual type `"terms"` is a matrix with one column for every term (excluding the intercept) in the model.
`p` | vector of percentiles. This is used only for quantile predictions.
`...` | other arguments

### Value

a vector or matrix of predicted values.

### References

Escobar and Meeker (1992). Assessing influence in regression analysis with censored data. Biometrics, 48, 507-528.

### See Also

`survreg`, `residuals.survreg`

### Examples

    # Draw figure 1 from Escobar and Meeker
    data(stanford2)
    fit <- survreg(Surv(time,status) ~ age + age^2, data=stanford2,
            dist='lognormal')
    plot(stanford2$age, stanford2$time, xlab='Age', ylab='Days',
            xlim=c(0,65), ylim=c(.01, 10^6), log='y')
    pred <- predict(fit, newdata=list(age=1:65), type='quantile',
                     p=c(.1, .5, .9))
    matlines(1:65, pred, lty=c(2,1,2), col=1)

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