ljrkk {ljr} | R Documentation |
This function performs the likelihood ratio tests to find p-values in testing the significance of each of the coefficients as well as the intercept and ordered observation times. The p-values are determined by a Monte Carlo method.
ljrkk(k,y,n,tm,X,ofst,R=1000)
k |
the pre-specified number of joinpoints (with unknown locations). |
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. |
The re-weighted log-likelihood is the log-likelihood divided by the largest component of n.
pvals |
The estimates of the p-values via simulation. |
The authors are Michal Czajkowski, Ryan Gill, and Greg Rempala. The software is maintained by Ryan Gill rsgill01@louisville.edu.
Czajkowski, M., Gill, R. and Rempala, G. (2007). Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns. To appear.
N=20 m=2 k=1 beta=c(0.1,0.1,-0.05) gamma=c(0.1,-0.05) tau=c(5) ofst=runif(N,-2.5,-1.5) x1=round(runif(N,-0.5,9.5)) x2=round(runif(N,-0.5,9.5)) X=cbind(x1,x2) n=rep(1e9,N) tm=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10) eta=ofst+beta[1]+gamma[1]*tm if (m>0) for (i in 1:m) eta=eta+beta[i+1]*X[,i] if (k>0) for (i in 1:k) eta=eta+gamma[i+1]*pmax(tm-tau[i],0) y=rbinom(N,size=n,prob=exp(eta)/(1+exp(eta))) temp.ljr=ljrkk(1,y,n,tm,X,ofst,R=1000)