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
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# Data from code 6.9
library(MASS)
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set.seed(141)
nobs <- 2500
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x1 <- rbinom(nobs,size=1, prob=0.6)
x2 <- runif(nobs)
xb <- 1 + 2.0*x1 - 1.5*x2
a <- 3.3
theta <- 0.303 # 1/a
exb <- exp(xb)
nby <- rnegbin(n=nobs, mu=exb, theta=theta)
negbml <- data.frame(nby, x1, x2)
Code 6.10 Negative binomial model in R using COUNT
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library(COUNT)
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nb3 <- nbinomial(nby ~ x1 + x2, data=negbml)
summary(nb3)
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Output on screen:
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Call:
nbinomial(formula1 = nby ~ x1 + x2, data = negbml)
Deviance Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.58697 -1.12176 -0.78956 -0.48925 0.06661 3.05031
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Pearson Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.539910 -0.510899 -0.445391 0.000391 0.069375 10.176786
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Coefficients (all in linear predictor):
Estimate SE Z p LCL UCL
(Intercept) 0.986 0.0901 10.9 7.32e-28 0.809 1.16
x1 2.040 0.0807 25.3 8.08e-141 1.882 2.20
x2 -1.613 0.1373 -11.7 7.26e-32 -1.882 -1.34
(Intercept)_s 3.382 0.1232 27.4 7.53e-166 3.140 3.62
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Null deviance: 3030.919 on 2498 d.f.
Residual deviance: 2355.517 on 2496 d.f.
Null Pearson: 4726.694 on 2498 d.f.
Residual Pearson: 2474.729 on 2496 d.f.
Dispersion: 0.9914778
AIC: 11552.61
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Number of optimizer iterations: 88