\name{CameronTrivedi1998}
\alias{CameronTrivedi1998}

\title{Data and Examples from Cameron and Trivedi (1998)}

\description{
This manual page collects a list of examples from the book. Some 
solutions might not be exact and the list is certainly not complete.
If you have suggestions for improvement (preferably in the form of code),
please contact the package maintainer.
}

\references{
Cameron, A.C. and Trivedi, P.K. (1998). \emph{Regression Analysis of Count Data}. 
Cambridge: Cambridge University Press.
}

\seealso{\code{\link{DoctorVisits}}, \code{\link{NMES1988}}, \code{\link{RecreationDemand}}}

\examples{
\dontshow{ if(!requireNamespace("MASS") ||
              !requireNamespace("pscl")) {
  if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) {
    stop("not all packages required for the example are installed")
  } else q() }}
\donttest{
library("MASS")
library("pscl")

###########################################
## Australian health service utilization ##
###########################################

## data
data("DoctorVisits", package = "AER")

## Poisson regression
dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson)
dv_qpois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = quasipoisson)

## Table 3.3 
round(cbind(
  Coef = coef(dv_pois),
  MLH = sqrt(diag(vcov(dv_pois))),
  MLOP = sqrt(diag(vcovOPG(dv_pois))),
  NB1 = sqrt(diag(vcov(dv_qpois))),
  RS = sqrt(diag(sandwich(dv_pois)))
), digits = 3)

## Table 3.4
## NM2-ML
dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits)
summary(dv_nb)
## NB1-GLM = quasipoisson
summary(dv_qpois)

## overdispersion tests (page 79)
lrtest(dv_pois, dv_nb) ## p-value would need to be halved
dispersiontest(dv_pois, trafo = 1)
dispersiontest(dv_pois, trafo = 2)


##########################################
## Demand for medical care in NMES 1988 ##
##########################################

## select variables for analysis
data("NMES1988", package = "AER")
nmes <- NMES1988[,-(2:6)]

## dependent variable
## Table 6.1
table(cut(nmes$visits, c(0:13, 100)-0.5, labels = 0:13))

## NegBin regression
nmes_nb <- glm.nb(visits ~ ., data = nmes)

## NegBin hurdle
nmes_h <- hurdle(visits ~ ., data = nmes, dist = "negbin")

## from Table 6.3
lrtest(nmes_nb, nmes_h)

## from Table 6.4
AIC(nmes_nb)
AIC(nmes_nb, k = log(nrow(nmes)))
AIC(nmes_h)
AIC(nmes_h, k = log(nrow(nmes)))

## Table 6.8
coeftest(nmes_h, vcov = sandwich)
logLik(nmes_h)
1/nmes_h$theta


###################################################
## Recreational boating trips to Lake Somerville ##
###################################################

## data
data("RecreationDemand", package = "AER")

## Poisson model:
## Cameron and Trivedi (1998), Table 6.11
## Ozuna and Gomez (1995), Table 2, col. 3
fm_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson)
summary(fm_pois)
logLik(fm_pois)
coeftest(fm_pois, vcov = sandwich)

## Negbin model:
## Cameron and Trivedi (1998), Table 6.11
## Ozuna and Gomez (1995), Table 2, col. 5
library("MASS")
fm_nb <- glm.nb(trips ~ ., data = RecreationDemand)
coeftest(fm_nb, vcov = vcovOPG)
logLik(fm_nb)

## ZIP model:
## Cameron and Trivedi (1998), Table 6.11
fm_zip <- zeroinfl(trips ~  . | quality + income, data = RecreationDemand)
summary(fm_zip)
logLik(fm_zip)

## Hurdle models
## Cameron and Trivedi (1998), Table 6.13
## poisson-poisson
sval <- list(count = c(2.15, 0.044, .467, -.097, .601, .002, -.036, .024), 
             zero = c(-1.88, 0.815, .403, .01, 2.95, 0.006, -.052, .046))
fm_hp0 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
  zero = "poisson", start = sval, maxit = 0)
fm_hp1 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
  zero = "poisson", start = sval)
fm_hp2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
  zero = "poisson")
sapply(list(fm_hp0, fm_hp1, fm_hp2), logLik)

## negbin-negbin
fm_hnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin")
summary(fm_hnb)
logLik(fm_hnb)

sval <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), 
             zero = c(-3.046, 4.638, -.025, .026, 16.203, 0.030, -.156, .117),
             theta = c(count = 1/1.7, zero = 1/5.609))
fm_hnb2 <- try(hurdle(trips ~ ., data = RecreationDemand,
  dist = "negbin", zero = "negbin", start = sval))
if(!inherits(fm_hnb2, "try-error")) {
summary(fm_hnb2)
logLik(fm_hnb2)
}

## geo-negbin
sval98 <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), 
             zero = c(-2.88, 1.44, .4, .03, 9.43, 0.01, -.08, .071),
             theta = c(count = 1/1.7))
sval96 <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), 
             zero = c(-2.882, 1.437, .406, .026, 11.936, 0.008, -.081, .071),
             theta = c(count = 1/1.7))
      
fm_hgnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "geometric")
summary(fm_hgnb)
logLik(fm_hgnb)

## logLik with starting values from Gurmu + Trivedi 1996
fm_hgnb96 <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "geometric",
                  start = sval96, maxit = 0)
logLik(fm_hgnb96)

## logit-negbin
fm_hgnb2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin")
summary(fm_hgnb2)
logLik(fm_hgnb2)

## Note: quasi-complete separation
with(RecreationDemand, table(trips > 0, userfee))
}
}

\keyword{datasets}
