# 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`
[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.