multinb.fit: Multivariate negative binomial model with robust estimation... In multinbmod: Regression analysis of overdispersed correlated count data

Description

This function is called by "multinbmod", but it can also be called directly

Usage

 1 multinb.fit(y, x, offset=1, id, start.par, control=list())

Arguments

 y Response vector. x Design matrix of covariates. offset Optional vector of offset values. id Variable indicating which subjects are correlated. start.par Vector of starting values for the parameters in the linear predictor (defaults to zero) and the overdispersion parameter (default to 0.5). control A list of parameters that control the convergence criteria. See "nlminb" for details.

Value

The return values is a list with components:

 estimated regression coefficients se from model Estimated standard errors of regression coefficients. robust se Robust estimate of standard errors of regression coefficients. t-values Robust t-values. covariance of beta estimates from model Estimated covariance of estimated regression parameters. robust covariance of beta estimates Robust estimate of covariance of estimated regression coefficients estimated phi ML estimate of overdisperision parameter. se(phi) Its standard error. -2 x log-likelihood converged? Logical. iterations Number of iterations required for convergence.

Author(s)

Ivonne Solis-Trapala

References

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.

multinbmod

Examples

 1 2 3 4 5 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) multinb.fit(y,cbind(1,x),id=id)

Example output

\$`estimated regression coefficients`
V1          x
1.5924262 -0.1352033

\$`se from model`
V1         x
0.1569822 0.1042238

\$`robust se`
V1         x
0.1615292 0.1020012

\$`robust t-values`
V1         x
9.858443 -1.325507

\$`covariance of beta estimates from model`
x
0.02464341 -0.00470229
x -0.00470229  0.01086260

\$`robust covariance of beta estimates`
x
0.02609168 -0.00622219
x -0.00622219  0.01040424

\$`estimated phi`
 0.4089209

\$`se(phi)`
 0.1362344

\$`-2 x loglikelihood`
 614.4774

\$`converged?`
 TRUE

\$iterations
 11

multinbmod documentation built on May 2, 2019, 4:21 a.m.