survival (version 2.36-5)

predict.coxph: Predictions for a Cox model

Description

Compute fitted values and regression terms for a model fitted by coxph

Usage

## S3 method for class 'coxph':
predict(object, newdata,
type=c("lp", "risk", "expected", "terms"),
se.fit=FALSE, na.action=na.pass, terms=names(object$assign), collapse, ...)

Arguments

object

the results of a coxph fit.

newdata

Optional new data at which to do predictions. If absent predictions are for the data frame used in the original fit.

type

the type of predicted value. Choices are the linear predictor ("lp"), the risk score exp(lp) ("risk"), the expected number of events given the covariates and follow-up time ("expected"), and the terms of the linea

se.fit

if TRUE, pointwise standard errors are produced for the 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.

terms

if type="terms", this argument can be used to specify which terms should be included; the default is all.

collapse

optional vector of subject identifiers. If specified, the output will contain one entry per subject rather than one entry per observation.

...

For future methods

Value

• a vector or matrix of predictions, or a list containing the predictions (element "fit") and their standard errors (element "se.fit") if the se.fit option is TRUE.

Details

The linear predictors for each strata are centered within each strata of the model. Two practical reasons are for numerical stability (avoid large arguments to the exp function) and reproducability (addition of a constant to the a covariate causes no change). The primary underlying reason is statistical: a Cox model only predicts relative risks between pairs of subjects in the same strata, and hence the addition of a constant to the linear predictor, either overall or only within a particular stratum, has no effect on any downstream computations.

See Also

predict,coxph,termplot

Examples

fit <- coxph(Surv(time, status) ~ age + ph.ecog + strata(inst), lung) 
mresid <- lung$status - predict(fit, type='expected') #Martingale resid 
predict(fit,type="lp")
predict(fit,type="expected")
predict(fit,type="risk",se.fit=TRUE)
predict(fit,type="terms",se.fit=TRUE)