COUNT (version 1.3.4)

nbc_syn: Negative binomial (NB-C): generic synthetic canonical negative binomial data and model

Description

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

Usage

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

Value

nbcy

Canonical negative binomial (NB-C) response; number of counts

sim.data

synthetic data set

Arguments

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, ...)

Author

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

Details

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.

References

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

See Also

nb2_syn, nb1_syn

Examples

if (FALSE) {
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
}