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), na.action=na.pass, ...)

### 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.
`na.action` | applies only when the `newdata` argument is present, and defines the missing value action for the new data. The default is to include all observations.
`...` | for future methods

### 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, 1992.
    fit <- survreg(Surv(time,status) ~ age + I(age^2), data=stanford2,
    	dist='lognormal')
    with(stanford2, plot(age, time, xlab='Age', ylab='Days',
    	xlim=c(0,65), ylim=c(.1, 10^5), log='y', type='n'))
    with(stanford2, points(age, time, pch=c(2,4)[status+1], cex=.7))
    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)

    # Predicted Weibull survival curve for a lung cancer subject with
    #  ECOG score of 2
    lfit <- survreg(Surv(time, status) ~ ph.ecog, data=lung)
    pct <- 1:98/100   # The 100th percentile of predicted survival is at +infinity
    ptime <- predict(lfit, newdata=data.frame(ph.ecog=2), type='quantile',
                     p=pct, se=TRUE)
    matplot(cbind(ptime$fit, ptime$fit + 2*ptime$se.fit,
                             ptime$fit - 2*ptime$se.fit)/30.5, 1-pct,
            xlab="Months", ylab="Survival", type='l', lty=c(1,2,2), col=1)

[Package survival version 2.41-3 Index]