R in a Nutshell

Survival Models

Survival analysis is concerned with looking at the amount of time that elapses before an event occurs. An obvious application is to look at mortality statistics (predicting how long people live), but it can also be applied to mechanical systems (the time before a failure occurs), marketing (the amount of time before a consumer cancels an account), or other areas.

In R, there are a variety of functions in the survival library for modeling survival data.

To estimate a survival curve for censored data, you can use the survfit function:

survfit(formula, data, weights, subset, na.action,  
        etype, id, ...)

This function accepts the following arguments.

Argument	Description
formula	Describes the relationship between the response value and the predictors. The response value should be a Surv object.
data	The data frame in which to evaluate formula.
weights	Weights for observations.
subset	Subset of observation to use in fitting the model.
na.action	Function to deal with missing values.
etype	The variable giving the type of event.
id	The variable that identifies individual subjects.
type	Specifies the type of survival curve. Options include "kaplan-meier", "fleming-harrington", and "fh2".
error	Specifies the type of error. Possible values are "greenwood" for the Greenwood formula or "tsiatis" for the Tsiatis formula.
conf.type	Confidence interval type. One of "none", "plain", "log" (the default), or "log-log".
conf.lower	A character string to specify modified lower limits to the curve, the upper limit ...