survfit.cph

a fit object from <code>cph</code> or <code>coxph</code>
see <code><a href="/link/survfit.coxph?package=rms&version=2.0-2&to=survival" rd-options="survival" data-mini-rdoc="survival::survfit.coxph">survfit.coxph</a></code>

formula

newdata

se.fit

conf.int

individual

type

vartype

see <code><a href="/link/survfit?package=rms&version=2.0-2&to=survival" rd-options="survival" data-mini-rdoc="survival::survfit">survfit</a></code>

conf.type

This is a slightly modified version of Therneau's <code>survfit.coxph</code>
function.  The difference is that <code>survfit.cph</code> assumes that
<code>x=TRUE,y=TRUE</code> were specified to the fit. This assures that the
environment in effect at the time of the fit (e.g., automatic knot
estimation for spline functions) is the same one used for basing predictions.

survival

Regression modeling, testing, estimation, validation,
        graphics, prediction, and typesetting by storing enhanced model
        design attributes in the fit.  rms is a collection of about 180
        functions that assist and streamline modeling, especially for
        biostatistical and epidemiologic applications.  It also
        contains new functions for binary and ordinal logistic
        regression models and the Buckley-James multiple regression
        model for right-censored responses, and implements penalized
        maximum likelihood estimation for logistic and ordinary linear
        models.  rms works with almost any regression model, but it was
        especially written to work with binary or ordinal logistic
        regression, Cox regression, accelerated failure time models,
        ordinary linear models, the Buckley-James model, generalized
        least squares for serially or spatially correlated
        observations, generalized linear models, and quantile
        regression.

survfit.cph: Cox Predicted Survival

Description

Usage

Arguments

Value

See Also