Predicted Values for a survReg Object

DESCRIPTION:

Compute predicted values for a survReg object.

USAGE:

predict.survReg(object, newdata, type=c("response", "link", "linear",  
     "response", "terms", "quantile", "uquantile"), 
     se.fit=F, terms=labels.lm(object), p=c(0.1, 0.9)) 

REQUIRED ARGUMENTS:

object
result of a model fit using the survReg function.

OPTIONAL ARGUMENTS:

newdata
data for prediction. If missing, 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.

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:

,

EXAMPLES:

# Draw figure 1 from Escobar and Meeker 
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)