ljr (version 1.1-0)

ljr0: MLE with 0 joinpoints

Description

Determines the maximum likelihood estimate of model coefficients in the logistic joinpoint regression model with no joinpoints.

Usage

ljr0(y,n,tm,X,ofst)

Arguments

y

the vector of Binomial responses.

n

the vector of sizes for the Binomial random variables.

tm

the vector of observation times.

X

a design matrix containing other covariates.

ofst

a vector of known offsets for the logit of the response.

Value

• Coef
  A table of coefficient estimates.
• 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

ljr01,ljr02,ljrb2,ljrf2,ljrb,ljrf

Examples

N=20
 m=2
 k=0
 beta=c(0.1,0.1,-0.05)
 gamma=c(0.1)
 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=ljr0(y,n,tm,X,ofst)