NMES1988 | R Documentation |

Cross-section data originating from the US National Medical Expenditure Survey (NMES) conducted in 1987 and 1988. The NMES is based upon a representative, national probability sample of the civilian non-institutionalized population and individuals admitted to long-term care facilities during 1987. The data are a subsample of individuals ages 66 and over all of whom are covered by Medicare (a public insurance program providing substantial protection against health-care costs).

`data("NMES1988")`

A data frame containing 4,406 observations on 19 variables.

- visits
Number of physician office visits.

- nvisits
Number of non-physician office visits.

- ovisits
Number of physician hospital outpatient visits.

- novisits
Number of non-physician hospital outpatient visits.

- emergency
Emergency room visits.

- hospital
Number of hospital stays.

- health
Factor indicating self-perceived health status, levels are

`"poor"`

,`"average"`

(reference category),`"excellent"`

.- chronic
Number of chronic conditions.

- adl
Factor indicating whether the individual has a condition that limits activities of daily living (

`"limited"`

) or not (`"normal"`

).- region
Factor indicating region, levels are

`northeast`

,`midwest`

,`west`

,`other`

(reference category).- age
Age in years (divided by 10).

- afam
Factor. Is the individual African-American?

- gender
Factor indicating gender.

- married
Factor. is the individual married?

- school
Number of years of education.

- income
Family income in USD 10,000.

- employed
Factor. Is the individual employed?

- insurance
Factor. Is the individual covered by private insurance?

- medicaid
Factor. Is the individual covered by Medicaid?

Journal of Applied Econometrics Data Archive for Deb and Trivedi (1997).

http://qed.econ.queensu.ca/jae/1997-v12.3/deb-trivedi/

Cameron, A.C. and Trivedi, P.K. (1998). *Regression Analysis of Count Data*.
Cambridge: Cambridge University Press.

Deb, P., and Trivedi, P.K. (1997). Demand for Medical Care by the
Elderly: A Finite Mixture Approach. *Journal of Applied Econometrics*,
**12**, 313–336.

Zeileis, A., Kleiber, C., and Jackman, S. (2008). Regression Models
for Count Data in R. *Journal of Statistical Software*, **27**(8).
doi:10.18637/jss.v027.i08.

`CameronTrivedi1998`

```
## packages
library("MASS")
library("pscl")
## select variables for analysis
data("NMES1988")
nmes <- NMES1988[, c(1, 7:8, 13, 15, 18)]
## dependent variable
hist(nmes$visits, breaks = 0:(max(nmes$visits)+1) - 0.5)
plot(table(nmes$visits))
## convenience transformations for exploratory graphics
clog <- function(x) log(x + 0.5)
cfac <- function(x, breaks = NULL) {
if(is.null(breaks)) breaks <- unique(quantile(x, 0:10/10))
x <- cut(x, breaks, include.lowest = TRUE, right = FALSE)
levels(x) <- paste(breaks[-length(breaks)], ifelse(diff(breaks) > 1,
c(paste("-", breaks[-c(1, length(breaks))] - 1, sep = ""), "+"), ""), sep = "")
return(x)
}
## bivariate visualization
par(mfrow = c(3, 2))
plot(clog(visits) ~ health, data = nmes, varwidth = TRUE)
plot(clog(visits) ~ cfac(chronic), data = nmes)
plot(clog(visits) ~ insurance, data = nmes, varwidth = TRUE)
plot(clog(visits) ~ gender, data = nmes, varwidth = TRUE)
plot(cfac(visits, c(0:2, 4, 6, 10, 100)) ~ school, data = nmes, breaks = 9)
par(mfrow = c(1, 1))
## Poisson regression
nmes_pois <- glm(visits ~ ., data = nmes, family = poisson)
summary(nmes_pois)
## LM test for overdispersion
dispersiontest(nmes_pois)
dispersiontest(nmes_pois, trafo = 2)
## sandwich covariance matrix
coeftest(nmes_pois, vcov = sandwich)
## quasipoisson model
nmes_qpois <- glm(visits ~ ., data = nmes, family = quasipoisson)
## NegBin regression
nmes_nb <- glm.nb(visits ~ ., data = nmes)
## hurdle regression
nmes_hurdle <- hurdle(visits ~ . | chronic + insurance + school + gender,
data = nmes, dist = "negbin")
## zero-inflated regression model
nmes_zinb <- zeroinfl(visits ~ . | chronic + insurance + school + gender,
data = nmes, dist = "negbin")
## compare estimated coefficients
fm <- list("ML-Pois" = nmes_pois, "Quasi-Pois" = nmes_qpois, "NB" = nmes_nb,
"Hurdle-NB" = nmes_hurdle, "ZINB" = nmes_zinb)
round(sapply(fm, function(x) coef(x)[1:7]), digits = 3)
## associated standard errors
round(cbind("ML-Pois" = sqrt(diag(vcov(nmes_pois))),
"Adj-Pois" = sqrt(diag(sandwich(nmes_pois))),
sapply(fm[-1], function(x) sqrt(diag(vcov(x)))[1:7])),
digits = 3)
## log-likelihoods and number of estimated parameters
rbind(logLik = sapply(fm, function(x) round(logLik(x), digits = 0)),
Df = sapply(fm, function(x) attr(logLik(x), "df")))
## predicted number of zeros
round(c("Obs" = sum(nmes$visits < 1),
"ML-Pois" = sum(dpois(0, fitted(nmes_pois))),
"Adj-Pois" = NA,
"Quasi-Pois" = NA,
"NB" = sum(dnbinom(0, mu = fitted(nmes_nb), size = nmes_nb$theta)),
"NB-Hurdle" = sum(predict(nmes_hurdle, type = "prob")[,1]),
"ZINB" = sum(predict(nmes_zinb, type = "prob")[,1])))
## coefficients of zero-augmentation models
t(sapply(fm[4:5], function(x) round(x$coefficients$zero, digits = 3)))
```