RcmdrPlugin.survival (version 1.2-1)

plot.coxph: Plot Method for coxph Objects

Description

Plots the predicted survival function from a coxph object, setting covariates to particular values.

Usage

# S3 method for coxph
plot(x, newdata, typical = mean,  byfactors=FALSE, 
  col = palette(), lty,  conf.level = 0.95, ...)

Arguments

x

a coxph object.

newdata

a data frame containing (combinations of) values to which predictors are set; optional.

typical

function to use to compute "typical" values of numeric predictors.

byfactors

if TRUE, different lines are drawn for each unique combination of factor values, including strata; if FALSE (the default) distinct lines are drawn only for different strata, with all columns of the model matrix (including for factors) set to their means.

col

colors for lines.

lty

line-types for lines; if missing, defaults to 1 to number required.

conf.level

level for confidence intervals; note: whether or not confidence intervals are plotted is determined by plot.survfit, which plot.coxph calls; if a conf.int argument is supplied it is passed through.

…

arguments passed to plot.

Value

Invisibly returns the summary resulting from applying survfit.coxph to the coxph object.

Details

If newdata is missing then all combinations of levels of factor-predictors (or strata), if present, are combined with "typical" values of numeric predictors.

References

John Fox, Marilia Sa Carvalho (2012). The RcmdrPlugin.survival Package: Extending the R Commander Interface to Survival Analysis. Journal of Statistical Software, 49(7), 1-32.

See Also

coxph, survfit.coxph, plot.survfit.

Examples

# NOT RUN {
require(survival)
cancer$sex <- factor(ifelse(cancer$sex == 1, "male", "female"))

mod.1 <- coxph(Surv(time, status) ~ age + wt.loss, data=cancer)
plot(mod.1)
plot(mod.1, typical=function(x) quantile(x, c(.25, .75)))

mod.2 <- coxph(Surv(time, status) ~ age + wt.loss + sex, data=cancer)
plot(mod.2)

mod.3 <- coxph(Surv(time, status) ~ (age + wt.loss)*sex, data=cancer)
plot(mod.3)

mod.4 <- coxph(Surv(time, status) ~ age + wt.loss + strata(sex), data=cancer)
plot(mod.4)

mods.1 <- survreg(Surv(time, status) ~ age + wt.loss, data=cancer)
# }