ljrb {ljr}R Documentation

Perform backward joinpoint selection algorithm with upper bound K.

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 rsgill01@louisville.edu.

References

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

See Also

ljrk,ljrf

Examples

 data(kcm)
 attach(kcm) 
 set.seed(12345)
 ljrb(1,Count,Population,Year+.5,R=20)

[Package ljr version 1.2-0 Index]