1d:T785,# Poisson : generic synthetic Poisson data and model poisson_syn is a generic function for developing synthetic Poisson data and a model given user defined specifications. ```r poisson_syn(nobs = 50000, off = 0, xv = c(1, -.5, 1)) ``` ## 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, ...) ## 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. ## Returns - **py**: Poisson response; number of counts - **sim.data**: synthetic data set ## References Hilbe, J.M. (2011), Negative Binomial Regression, second edition, Cambridge University Press. ## Author(s) Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of Technology Andrew Robinson, Universty of Melbourne, Australia. ## See Also `nb2_syn` ## Examples ```r # 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)) ## Not run: # default (without offset) sim.data <- poisson_syn() dmypo <- glm( py ~ . , family=poisson, data = sim.data) summary(dmypo) ## End(Not run) ```

poisson_syn function