HSI
From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press
(c) 2017, Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida
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
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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)
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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 ***
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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