nbc_syn: Negative binomial (NB-C): generic synthetic canonical...
In COUNT: Functions, Data and Code for Count Data

Description Usage Arguments Details Value Author(s) References See Also Examples

nbc_syn is a generic function for developing synthetic NB-C data and a model given user defined specifications.

1	nbc_syn(nobs=50000, alpha=1.15, xv = c(-1.5, -1.25, -.1))

`nobs`	number of observations in model, Default is 50000
`alpha`	NB-C heterogeneity or ancillary parameter
`xv`	predictor coefficient values. First argument is intercept. Use as xv = c(intercept , x1_coef, x2_coef, ...)

Create a synthetic canonial negative binomial (NB-C) regression model using the appropriate arguments. Model data with predictors indicated as a group with a period (.). Data can be modeled using the ml.nbc.r function in the COUNT package. See examples.

`nbcy`	Canonical negative binomial (NB-C) response; number of counts
`sim.data`	synthetic data set

Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of Technology Andrew Robinson, Universty of Melbourne, Australia.

Hilbe, J.M. (2011), Negative Binomial Regression, second edition, Cambridge University Press.

nb2_syn, nb1_syn

## Not run: 
sim.data <- nbc_syn(nobs = 50000, alpha = 1.15, xv = c(-1.5, -1.25, -.1))
mynbc <- ml.nbc(nbcy ~ . , data = sim.data)
mynbc

# default
sim.data <- nbc_syn()
dnbc <- ml.nbc(nbcy ~ . , data = sim.data)
dnbc

## End(Not run)