predict.survreg: 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 ("quan

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.

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.

Examples

Run this code

# 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)