ljrk: MLE with k joinpoints

In ljr: Logistic Joinpoint Regression

Description

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

Usage

ljrk(k,y,n,tm,X,ofst)

Arguments

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.

Details

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

Value

Coef	A table of coefficient estimates.
Joinpoints	The estimates of the joinpoints.
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

ljrb,ljrf

Examples

 data(kcm)
 attach(kcm) 
 ljrk(1,Count,Population,Year+.5)

Example output

ljr 1.4-0 loaded
Model:
y~Binom(n,p) where p=invlogit(eta)
eta=b0+g0*t+g1*max(t-tau1,0)

       Variables         Coef
b0     Intercept -40.81272431
g0             t   0.01737196
g1 max(t-tau1,0)  -0.02418284

Joinpoints:
                
1 tau1= 2001.273
$Coef
    Intercept             t max(t-tau1,0) 
 -40.81272431    0.01737196   -0.02418284 

$Joinpoints
   tau1= 
2001.273 

$wlik
[1] -0.112523

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