Nothing
# syn.ppfm.r Synthetic Poisson-Poisson Finite Mixture model
# Table 13.2: Hilbe, Negative Binomial Regression, 2 ed, Cambridge Univ Press
#
library(flexmix)
nobs <- 50000
x1 <- runif(nobs)
x2 <- runif(nobs)
xb1 <- 1 + 0.25*x1 - 0.75*x2
xb2 <- 2 + 0.75*x1 - 1.25*x2
exb1 <- exp(xb2)
exb2 <- exp(xb1)
py1 <- rpois(nobs, exb2)
py2 <- rpois(nobs, exb1)
poixpoi <- py2
poixpoi <- ifelse(runif(nobs) > .9, py1, poixpoi)
pxp <- flexmix(poixpoi ~ x1 + x2, k=2,
model=FLXMRglm(family="poisson"))
summary(pxp)
parameters(pxp, component=1, model=1)
parameters(pxp, component=2, model=1)
library(MASS)
library(gamlss)
nobs <- 50000
x1 <- runif(nobs)
x2 <- runif(nobs)
xb <- 0.5 + 1.25*x1 - 1.5*x2
delta <- .5 # value assigned to delta
exb <-exp(xb)
idelta <- (1/delta)*exb
xg <-rgamma(50000, idelta, idelta, 1/idelta)
xbg <- exb*xg
nb1y <- rpois(50000, xbg)
jhgm <- gamlss(nbly ~ x1 + x2,family=NBII)
summary(jhgm)
# nb1 <- ml.nb1(los ~ hmo + white + type) # Table 10.8
# nb1
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.