Synthetic Poisson data and model in R: binary and continuous predictor

From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press

you are kindly asked to include the complete citation if you used this material in a publication

Code 6.2 Synthetic Poisson data and model in R: binary and continuous predictors

============================================================

set.seed(18472)
nobs <- 750

x1_2 <- rbinom(nobs,size=1,prob=0.7)
x2 <- rnorm(nobs,0,1)
xb <- 1 - 1.5*x1_2 - 3.5*x2

exb <- exp(xb)
py <- rpois(nobs, exb)
pois <- data.frame(py, x1_2, x2)

poi <- glm(py ~ x1_2 + x2, family=poisson, data=pois)

summary(poi)

============================================================

Output on screen:

Call:

glm(formula = py ~ x1_2 + x2, family = poisson, data = pois)

Deviance Residuals:

Min 1Q Median 3Q Max

-3.5920 -0.5973 -0.1834 0.2879 3.5145

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 1.003426 0.010634 94.36 <2e-16 ***

x1_2 -1.507078 0.004833 -311.81 <2e-16 ***

x2 -3.500726 0.004228 -828.00 <2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 1821132.15 on 749 degrees of freedom

Residual deviance: 598.59 on 747 degrees of freedom

AIC: 2439.6

Number of Fisher Scoring iterations: 4

HSI