survfit.cph

a fit object from <code>cph</code> or <code>coxph</code>
see <code><a href="/link/survfit.coxph?package=rms&version=4.1-0&to=survival" rd-options="survival" data-mini-rdoc="survival::survfit.coxph">survfit.coxph</a></code>

formula

see
  <code><a href="/link/survfit?package=rms&version=4.1-0&to=survival" rd-options="survival" data-mini-rdoc="survival::survfit">survfit</a></code>.  If <code>individual</code> is <code>TRUE</code>,
  there must be exactly one <code>Srv</code> or <code>Surv</code> object in
  <code>newdata</code>.  This 
  object is used to specif

newdata,se.fit,conf.int,individual,type,vartype,conf.type,id

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 functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, 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 regression
	models, 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