# jackknife: Compute jackknife pseudo values. In prodlim: Product-Limit Estimation for Censored Event History Analysis

 jackknife R Documentation

## Compute jackknife pseudo values.

### Description

Compute jackknife pseudo values.

### Usage

``````jackknife(object, times, cause, keepResponse = FALSE, ...)
``````

### Arguments

 `object` Object of class `"prodlim"`. `times` Time points at which to compute pseudo values. `cause` Character (other classes are converted with `as.character`). For competing risks the cause of failure. `keepResponse` If `TRUE` add the model response, i.e. event time, event status, etc. to the result. `...` not used

### Details

Compute jackknife pseudo values based on marginal Kaplan-Meier estimate of survival, or based on marginal Aalen-Johansen estimate of the absolute risks, i.e., the cumulative incidence function.

### Note

The R-package pseudo does a similar job, and appears to be a little faster in small samples, but much slower in large samples. See examples.

### Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

### References

Andersen PK & Perme MP (2010). Pseudo-observations in survival analysis Statistical Methods in Medical Research, 19(1), 71-99.

`prodlim`

### Examples

``````

## pseudo-values for survival models

d=SimSurv(20)
f=prodlim(Hist(time,status)~1,data=d)
jackknife(f,times=c(3,5))

## in some situations it may be useful to attach the
## the event time history to the result
jackknife(f,times=c(3,5),keepResponse=TRUE)

# pseudo-values for competing risk models
set.seed(15)
d=SimCompRisk(15)
f=prodlim(Hist(time,event)~1,data=d)
jackknife(f,times=c(3,5),cause=1)
jackknife(f,times=c(1,3,5),cause=2)

``````

prodlim documentation built on Aug. 28, 2023, 5:07 p.m.