ljrb: Perform backward joinpoint selection algorithm with upper...

In ljr: Logistic Joinpoint Regression

Description

This function performs the backward joinpoint selection algorithm with K maximum possible number of joinpoints based on the likelihood ratio test statistic. The p-value is determined by a Monte Carlo method.

Usage

ljrb(K,y,n,tm,X,ofst,R=1000,alpha=.05)

Arguments

K	the pre-specified maximum possible number of joinpoints
y	the vector of Binomial responses.
n	the vector of sizes for the Binomial random variables.
tm	the vector of ordered observation times.
X	a design matrix containing other covariates.
ofst	a vector of known offsets for the logit of the response.
R	number of Monte Carlo simulations.
alpha	significance level of the test.

Details

The re-weighted log-likelihood is the log-likelihood divided by the largest component of n.

Value

pvals	The estimates of the p-values via simulation.
Coef	A table of coefficient estimates.
Joinpoints	The estimates of the joinpoint, if it is significant.
wlik	The maximum value of the re-weighted log-likelihood.

Author(s)

The authors are Michal Czajkowski, Ryan Gill, and Greg Rempala. The software is maintained by Ryan Gill [e-mail].

References

Czajkowski, M., Gill, R. and Rempala, G. (2008). Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns. Statistics in Medicine 27, 1508-1526.

See Also

ljrk,ljrf

Examples

 data(kcm)
 attach(kcm) 
 set.seed(12345)
## Not run: ljrb(1,Count,Population,Year+.5,R=20)

Example output

ljr 1.4-0 loaded

ljr documentation built on May 1, 2019, 7:50 p.m.