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# syn.nb2nb2fm.r Synthetic NB2-NB2 Finite Mixture model
# Table 13.3: Hilbe, Negative Binomial Regression, 2 ed, Cambridge Univ Press
library(gamlss.mx)
nobs <- 50000
x1 <- (runif(nobs))
x2 <- qnorm(runif(nobs))
xb1 <- 1 + .25*x1 - .75*x2
xb2 <- 2 + .75*x1 - 1.25*x2
a1 <- .5
a2 <- 1.5
ia1 <- 1/a1
ia2 <- 1/a2
exb1 <- exp(xb1)
exb2 <- exp(xb2)
xg1 <- rgamma(n = nobs, shape = a1, rate = a1)
xg2 <- rgamma(n = nobs, shape = a2, rate = a2)
xbg1 <-exb1*xg1
xbg2 <-exb2*xg2
nby1 <- rpois(nobs, xbg1)
nby2 <- rpois(nnobs, xbg2)
nbxnb <- nby2
nbxnb <- ifelse(runif(nobs) > .9, nby1, nbxnb)
nxn <- gamlssNP(nbxnb~x1+x2, random=~1,family=NBI, K=2)
summary(nxn)
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