ljr (version 1.1-0)

ljr1: MLE with 1 joinpoint

Description

Determines the maximum likelihood estimates of model coefficients in the logistic joinpoint regression model with one joinpoint.

Usage

ljr1(y,n,tm,X,ofst,summ=TRUE)

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.

summ

a boolean indicator of whether summary tables should be returned.

Value

• Coef
  A table of coefficient estimates.
• Joinpoint
  The estimate of the joinpoint.
• wlik
  The maximum value of the re-weighted log-likelihood.

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

ljrb2,ljrf2,ljr01,ljr12,ljrb,ljrf

Examples

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(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=ljr1(y,n,tm,X,ofst)