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

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.

See Also

multinbmod

Examples

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`
[1] 0.4089209

$`se(phi)`
[1] 0.1362344

$`-2 x loglikelihood`
[1] 614.4774

$`converged?`
[1] TRUE

$iterations
[1] 11

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