ljr (version 1.1-0)

ljr22: Test coefficients conditioned on K=2 joinpoint.

Description

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.

Usage

ljr22(y,n,tm,X,ofst,R=1000)

Arguments

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.

Value

• pvals
  The estimates of the p-values via simulation.

Details

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

References

Czajkowski, M., Gill, R. and Rempala, G. (2007). Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns. To appear.

See Also

ljr2

Examples

N=20
 m=2
 k=2
 beta=c(0.1,0.1,-0.05)
 gamma=c(0.1,-0.1,0.05)
 tau=c(3,6.5)
 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(10000,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=ljr22(y,n,tm,X,ofst,R=1000)