The US National Health and Nutrition Examination Study (NHANES) is a survey data collected by the US National Center for Health Statistics. The survey data dates back to 1999, where individuals of all ages are interviewed in their home annually and complete the health examination component of the survey. The study variables include demographic variables (e.g. age and annual household income), physical measurements (e.g. BMI – body mass index), health variables (e.g. diabetes status), and lifestyle variables (e.g. smoking status). This data frame contains the following columns:
id: Individual identifier
age: Age
gender: Sex 1=male, 0=female
educ: Education is dichotomized into high school and above versus less than high school
race: categorical variable with five levels
income: Household income ($1000 per year) was reported as a range of values in dollar (e.g. 0–4999, 5000–9999, etc.) and had 10 interval categories.
Income: Household income ($1000 per year) was reported as a range of values in dollar (e.g. 0–4999, 5000–9999, etc.) and had 10 interval categories.
bmi: body mass index
sbp: systolic blood pressure
nhanesFormat
An object of class data.frame with 9643 rows and 9 columns.
References
Emmanuel O Ogundimu, Gary S Collins (2019). “A robust imputation method for missing responses and covariates in sample selection models.” Statistical methods in medical research, 28(1), 102--116. Roderick J Little, Nanhua Zhang (2011). “Subsample ignorable likelihood for regression analysis with missing data.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 60(4), 591--605. Mikhail Zhelonkin, Marc G. Genton, Elvezio Ronchetti (2019). ssmrob: Robust Estimation and Inference in Sample Selection Models. R package version 0.7, https://CRAN.R-project.org/package=ssmrob. Ott Toomet, Arne Henningsen (2008). “Sample Selection Models in R: Package sampleSelection.” Journal of Statistical Software, 27(7). https://www.jstatsoft.org/article/view/v027i07.
Examples
data("nhanes")
attach(nhanes)
#> The following objects are masked from Mroz87 (pos = 3):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 4):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 5):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 6):
#>
#> age, educ, income
#> The following objects are masked from Mroz87 (pos = 7):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 8):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 9):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 10):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 11):
#>
#> age, educ, income
hist(Income, prob= TRUE, breaks = seq(1, 99, 0.5), xlim = c(1,10),
ylim = c(0,0.35), main = "Histogram of Income", xlab = "Category")
data2 <- subset(nhanes, !is.na(sbp))
data3 <- subset(data2, !is.na(bmi))
attach(data3)
#> The following objects are masked from nhanes:
#>
#> Income, age, bmi, educ, gender, id, income, race, sbp
#> The following objects are masked from Mroz87 (pos = 4):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 5):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 6):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 7):
#>
#> age, educ, income
#> The following objects are masked from Mroz87 (pos = 8):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 9):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 10):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 11):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 12):
#>
#> age, educ, income
data <- data3
data$YS <- ifelse(is.na(data$Income),0,1)
data$educ <- ifelse(data$educ<=2,0,1)
attach(data)
#> The following objects are masked from data3:
#>
#> Income, age, bmi, educ, gender, id, income, race, sbp
#> The following objects are masked from nhanes:
#>
#> Income, age, bmi, educ, gender, id, income, race, sbp
#> The following objects are masked from Mroz87 (pos = 5):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 6):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 7):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 8):
#>
#> age, educ, income
#> The following objects are masked from Mroz87 (pos = 9):
#>
#> age, educ
#> The following objects are masked from MEPS2001 (pos = 10):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 11):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 12):
#>
#> age, educ, income
#> The following objects are masked from MEPS2001 (pos = 13):
#>
#> age, educ, income
selectionEq <- YS~age+gender+educ+race
outcomeEq <- sbp~age+gender+educ+bmi