NMES1988 {AER} | R Documentation

## Demand for Medical Care in NMES 1988

### Description

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).

### Usage

    data("NMES1988")

### Format

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?

### Source

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

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

### References

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/.

### See Also

`CameronTrivedi1998`

### Examples

    ## 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)))

[Package AER version 1.2-6 Index]