mboost (version 2.9-1)

survFit: Survival Curves for a Cox Proportional Hazards Model

Description

Computes the predicted survivor function for a Cox proportional hazards model.

Usage

# S3 method for mboost
survFit(object, newdata = NULL, ...)
# S3 method for survFit
plot(x, xlab = "Time", ylab = "Probability", …)

Arguments

object

an object of class mboost which is assumed to have a CoxPH family component.

newdata

an optional data frame in which to look for variables with which to predict the survivor function.

x

an object of class survFit for plotting.

xlab

the label of the x axis.

ylab

the label of the y axis.

...

additional arguments passed to callies.

Value

An object of class survFit containing the following components:

surv

the estimated survival probabilities at the time points given in time.

time

the time points at which the survivor functions are evaluated.

n.event

the number of events observed at each time point given in time.

Details

If newdata = NULL, the survivor function of the Cox proportional hazards model is computed for the mean of the covariates used in the blackboost, gamboost, or glmboost call. The Breslow estimator is used for computing the baseline survivor function. If newdata is a data frame, the predict method of object, along with the Breslow estimator, is used for computing the predicted survivor function for each row in newdata.

See Also

gamboost, glmboost and blackboost for model fitting.

Examples

# NOT RUN {
library("survival")
data("ovarian", package = "survival")

fm <- Surv(futime,fustat) ~ age + resid.ds + rx + ecog.ps
fit <- glmboost(fm, data = ovarian, family = CoxPH(),
    control=boost_control(mstop = 500))

S1 <- survFit(fit)
S1
newdata <- ovarian[c(1,3,12),]
S2 <- survFit(fit, newdata = newdata)
S2

plot(S1)
# }

Run the code above in your browser using DataCamp Workspace