MEPS data

Description

2008 MEPS data.

Usage

1

Format

meps is a 18592 row data frame with the following columns

bmi

body mass index.

age

age in years.

gender

equal to 1 if male.

race

levels: 2 white, 3 black, 4 native American, 5 others.

education

years of education.

health

levels: 5 excellent, 6 very good, 7 good, 8 fair, 9 poor.

limitation

equal to 1 if health limits physical activity.

region

levels: 2 northeast, 3 mid-west, 4 south, 5 west.

private

equal to 1 if individual has private health insurance.

visits.hosp

equal to 1 if at least one visit to hospital outpatient departments.

diabetes

equal to 1 if diabetic.

hypertension

equal to 1 if hypertensive.

hyperlipidemia

equal to 1 if hyperlipidemic.

income

income (000's).

Source

The data have been obtained from http://www.meps.ahrq.gov/.

References

Radice R., Marra G. and Wojtys M. (2016), Copula Regression Spline Models for Binary Outcomes. Statistics and Computing, 26(5), 981-995.

Examples

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## Not run:  

###################################################
###################################################

library("SemiParBIVProbit")
data("meps", package = "SemiParBIVProbit") 

###################################################
# Bivariate brobit models with endogenous treatment
###################################################

treat.eq <- private ~ s(bmi) + s(income) + s(age) + s(education) +
                      as.factor(health) + as.factor(race) +
                      as.factor(limitation) + as.factor(region) + 
                      gender  + hypertension + hyperlipidemia + diabetes
out.eq <- visits.hosp ~ private + s(bmi) + s(income) + s(age) + 
                        s(education) + as.factor(health) + 
                        as.factor(race) + as.factor(limitation) + 
                        as.factor(region) + gender + hypertension + 
                        hyperlipidemia + diabetes

f.list <- list(treat.eq, out.eq) 
bpN    <- SemiParBIVProbit(f.list, data = meps)
bpF    <- SemiParBIVProbit(f.list, data = meps, BivD = "F")
bpC0   <- SemiParBIVProbit(f.list, data = meps, BivD = "C0")
bpC180 <- SemiParBIVProbit(f.list, data = meps, BivD = "C180")
bpJ0   <- SemiParBIVProbit(f.list, data = meps, BivD = "J0")
bpJ180 <- SemiParBIVProbit(f.list, data = meps, BivD = "J180")
bpG0   <- SemiParBIVProbit(f.list, data = meps, BivD = "G0")
bpG180 <- SemiParBIVProbit(f.list, data = meps, BivD = "G180")

conv.check(bpJ0)

AIC(bpN, bpF, bpC0, bpC180, bpJ0, bpJ180, bpG0, bpG180) 

set.seed(1)
summary(bpJ0, cm.plot = TRUE, cex.axis = 1.6, 
        cex.lab = 1.6, cex.main = 1.7)

#dev.copy(postscript, "contplot.eps")
#dev.off()

par(mfrow = c(2, 2), mar = c(4.5, 4.5, 2, 2), 
    cex.axis = 1.6, cex.lab = 1.6)
plot(bpJ0, eq = 1, seWithMean = TRUE, scale = 0, shade = TRUE, 
     pages = 1, jit = TRUE)

#dev.copy(postscript, "spline1.eps")
#dev.off() 

par(mfrow = c(2, 2), mar = c(4.5, 4.5, 2, 2), 
    cex.axis = 1.6, cex.lab = 1.6)
plot(bpJ0, eq = 2, seWithMean = TRUE, scale = 0, shade = TRUE, 
     pages = 1, jit = TRUE)

#dev.copy(postscript, "spline2.eps")
#dev.off() 

set.seed(1)
AT(bpJ0, nm.end = "private", hd.plot = TRUE, cex.axis = 1.5, 
   cex.lab = 1.5, cex.main = 1.6)

#dev.copy(postscript, "hd.plotAT.eps")
#dev.off()

AT(bpJ0, nm.end = "private", type = "univariate")

AT(bpJ0, nm.end = "private", type = "naive")


## End(Not run)

#

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