COUNT (version 1.3.4)

poisson_syn: Poisson : generic synthetic Poisson data and model

Description

poisson_syn is a generic function for developing synthetic Poisson data and a model given user defined specifications.

Usage

poisson_syn(nobs = 50000, off = 0, xv = c(1, -.5,  1))

Value

py

Poisson response; number of counts

sim.data

synthetic data set

Arguments

nobs

number of observations in model, Default is 50000

off

optional: log of offset variable

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 Poisson regression model using the appropriate arguments. Offset optional. Model data with predictors indicated as a group with a period (.). See examples.

References

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

See Also

nb2_syn

Examples

# standard Poisson model with two predictors and intercept
sim.data <- poisson_syn(nobs = 500, xv = c(2, .75, -1.25))
mypo <- glm(py ~ . , family=poisson, data = sim.data)
summary(mypo)
confint(mypo)

# Poisson with offset and three predictors
oset <- rep(1:5, each=100, times=1)*100 
loff <- log(oset)   
sim.data <- poisson_syn(nobs = 500, off = loff, xv = c(1.2, -.75, .25, -1.3))
mypof <- glm(py ~ . + loff, family=poisson, data = sim.data)
summary(mypof)
confint(mypof)

# Poisson without offset, exponentiated coefficients, CI's
sim.data <- poisson_syn(nobs = 500, xv = c(2, .75, -1.25))
mypo <- glm(py ~ . , family=poisson, data = sim.data)
exp(coef(mypo))
exp(confint(mypo))

if (FALSE) {
# default (without offset)
sim.data <- poisson_syn()
dmypo <- glm( py ~ . , family=poisson, data = sim.data)
summary(dmypo)
}