Description Details Author(s) References Examples
This is a robust likelihood approach for the regression analyis of overdispersed correlated counts data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
Package: | multinbmod |
Type: | Package |
Version: | 1.0 |
Date: | 2014-01-14 |
License: | GPL-2 |
LazyLoad: | yes |
Use function multinbmod to fit a multivariate negative binomial model by maximum likelihood. Robust estimates of regression parameters are provided.
Ivonne Solis-Trapala
Maintainer: Ivonne Solis-Trapala <i.solis-trapala@mrc-hnr.cam.ac.uk>
Solis-Trapala, I.L. and Farewell, V.T. (2005) Regression analysis of overdispersed correlated count data with subject specific covariates. Statistics in Medicine, 24: 2557-2575.
1 2 3 4 5 6 | id <- factor(rep(1:20, rep(5, 20)))
y <- rnbinom(100, mu = rexp(100,1)+rep(rexp(20,.3),rep(5,20)),size=2.5)
x<-rbinom(100,1,.5)
dat <- data.frame(y = y, x = x, id = id)
multinbmod(y~x,data=dat,id=id)
summary(multinbmod(y~x,data=dat,id=id))
|
$converged
[1] TRUE
$coefficients
(Intercept) x
1.46320733 0.06777607
$model.coef.se
(Intercept) x
0.1877981 0.1059591
$robust.coef.se
(Intercept) x
0.1776678 0.1566795
$robust.t.values
(Intercept) x
8.2356382 0.4325778
$mle.phi
[1] 0.5919477
$phi.se
[1] 0.1919075
$minus2.loglik
[1] 578.8424
$iterations
[1] 12
$call
multinbmod(formula = y ~ x, data = dat, id = id)
attr(,"class")
[1] "multinbmod"
$call
multinbmod(formula = y ~ x, data = dat, id = id)
$converged
[1] TRUE
$coefficients
Estimate ModelSE RobustSE Robust.t
(Intercept) 1.46320733 0.1877981 0.1776678 8.2356382
x 0.06777607 0.1059591 0.1566795 0.4325778
$MLE_of_phi
[1] 0.5919477
$SE_of_phi
[1] 0.1919075
$minus2.loglik
[1] 578.8424
$iterations
[1] 12
attr(,"class")
[1] "summary.multinbmod"
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