Simulated Health Data for MAIHDA Examples
maihda_sim_data.RdA simulated dataset containing health outcomes and demographic variables for 500 individuals. This dataset is designed to demonstrate intersectional health inequalities suitable for MAIHDA analysis. The data includes main effects and intersectional effects between gender, race, and education.
Usage
data("maihda_sim_data")Format
A data frame with 500 observations on the following 6 variables:
idInteger identifier for each individual
genderCharacter variable indicating gender ("Male" or "Female")
raceCharacter variable indicating race/ethnicity ("White", "Black", "Hispanic", or "Asian")
educationCharacter variable indicating education level ("High School", "Some College", "College", or "Graduate")
ageNumeric variable for age in years (range: 18-80)
health_outcomeNumeric variable representing a health score (higher is better)
Details
The health outcome was simulated with:
Main effects of gender, race, education, and age
Intersectional effects (e.g., Black women, men with high school education)
Random noise with standard deviation of 10
The data demonstrates typical patterns in health inequalities research where outcomes vary both by individual characteristics and their intersections.
Examples
data(maihda_sim_data)
# View structure
str(maihda_sim_data)
#> 'data.frame': 500 obs. of 6 variables:
#> $ id : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ gender : chr "Male" "Male" "Male" "Female" ...
#> $ race : chr "White" "White" "White" "White" ...
#> $ education : chr "High School" "College" "High School" "Graduate" ...
#> $ age : num 63 44 51 48 43 44 57 43 21 43 ...
#> $ health_outcome: num 78.9 86.4 75.1 88.3 90.3 72.2 65.5 83.3 68.6 81.1 ...
# Summary statistics
summary(maihda_sim_data)
#> id gender race education
#> Min. : 1.0 Length:500 Length:500 Length:500
#> 1st Qu.:125.8 Class :character Class :character Class :character
#> Median :250.5 Mode :character Mode :character Mode :character
#> Mean :250.5
#> 3rd Qu.:375.2
#> Max. :500.0
#> age health_outcome
#> Min. :18.00 Min. : 43.60
#> 1st Qu.:37.00 1st Qu.: 67.80
#> Median :45.00 Median : 76.40
#> Mean :45.31 Mean : 75.95
#> 3rd Qu.:53.00 3rd Qu.: 84.42
#> Max. :80.00 Max. :109.70
# \donttest{
# Example MAIHDA analysis
library(MAIHDA)
# Create strata
strata_result <- make_strata(maihda_sim_data, vars = c("gender", "race"))
# Fit model
model <- fit_maihda(health_outcome ~ age + (1 | stratum),
data = strata_result$data)
# Summarize
summary_maihda(model)
#> MAIHDA Model Summary
#> ====================
#>
#> Variance Partition Coefficient (VPC/ICC):
#> Estimate: 0.1907
#>
#> Variance Components:
#> component variance sd proportion
#> Between-stratum (random) 26.27 5.125 0.1907
#> Within-stratum (residual) 111.49 10.559 0.8093
#> Total 137.76 11.737 1.0000
#>
#> Fixed Effects:
#> term estimate
#> (Intercept) 84.9021
#> age -0.2647
#>
#> Stratum Estimates (first 10):
#> stratum stratum_id label random_effect se lower_95 upper_95
#> 1 1 Female_Asian -1.310 3.1488 -7.482 4.8613
#> 2 2 Female_Black -6.536 1.4336 -9.346 -3.7263
#> 3 3 Female_Hispanic 1.357 1.7539 -2.081 4.7944
#> 4 4 Female_White 7.942 0.8502 6.275 9.6080
#> 5 5 Male_Asian -2.978 2.5427 -7.961 2.0059
#> 6 6 Male_Black -3.114 1.5202 -6.094 -0.1345
#> 7 7 Male_Hispanic -0.916 1.4079 -3.676 1.8435
#> 8 8 Male_White 5.556 0.8447 3.901 7.2119
# Visualize
plot_maihda(model, type = "caterpillar")
# }