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).
URL http://www.jstatsoft.org/v27/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)))