ljr0: MLE with 0 joinpoints

In ljr: Logistic Joinpoint Regression

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.

Details

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

Value

Coef	A table of coefficient estimates.
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 [e-mail].

References

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

See Also

ljr01,ljrb,ljrf

Examples

 data(kcm)
 attach(kcm) 
 ljr0(Count,Population,Year+.5)

Example output

ljr 1.4-0 loaded
Model:
y~Binom(n,p) where p=invlogit(eta)
eta=b0+g0*t

   Variables         Coef
b0 Intercept -4.190069631
g0         t -0.000935695
$Coef
   Intercept            t 
-4.190069631 -0.000935695 

$wlik
[1] -0.1125237

ljr documentation built on May 1, 2019, 7:50 p.m.