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

(c) 2017,  Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida  

 

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# Data from code 6.9

library(MASS)

set.seed(141)
nobs <- 2500

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

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

library(COUNT)

nb3 <- nbinomial(nby ~ x1 + x2, data=negbml)
summary(nb3)

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

 

Output on screen:

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

Pearson Residuals:

         Min.         1st Qu.         Median          Mean            3rd Qu.              Max.

-0.539910      -0.510899     -0.445391      0.000391      0.069375      10.176786

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

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

Number of optimizer iterations:     88