R/add_weights_strata.R
add_weights_strata.RdCreates weight based on dividing stratified population counts from the source population by surveyed counts in the sample population.
add_weights_strata(
x,
p,
...,
population = population,
surv_weight = "surv_weight",
surv_weight_ID = "surv_weight_ID"
)a data frame of survey data
a data frame containing population data for groups in ...
shared grouping columns across both x and p. These are used
to match the weights to the correct subset of the population.
the column in p that defines the population numbers
the name of the new column to store the weights. Defaults to "surv_weight".
the name of the new ID column to be created. Defaults to "surv_weight_ID"
# define a fake dataset of survey data
# including household and individual information
x <- data.frame(stringsAsFactors=FALSE,
cluster = c("Village A", "Village A", "Village A", "Village A",
"Village A", "Village B", "Village B", "Village B"),
household_id = c(1, 1, 1, 1, 2, 2, 2, 2),
eligibile_n = c(6, 6, 6, 6, 6, 3, 3, 3),
surveyed_n = c(4, 4, 4, 4, 4, 3, 3, 3),
individual_id = c(1, 2, 3, 4, 4, 1, 2, 3),
age_grp = c("0-10", "20-30", "30-40", "50-60", "50-60", "20-30",
"50-60", "30-40"),
sex = c("Male", "Female", "Male", "Female", "Female", "Male",
"Female", "Female"),
outcome = c("Y", "Y", "N", "N", "N", "N", "N", "Y")
)
# define a fake population data set
# including age group, sex, counts and proportions
p <- epikit::gen_population(total = 10000,
groups = c("0-10", "10-20", "20-30", "30-40", "40-50", "50-60"),
proportions = c(0.1, 0.2, 0.3, 0.4, 0.2, 0.1))
#> Warning: Given proportions (or counts) is not the same as
#> groups multiplied by strata length, they will be repeated to match
#> Warning: The sum of given proportions is more than 5% away from 100%
#> If the intention was to provide proportions by strata then ignore this message
# make sure col names match survey dataset
p <- dplyr::rename(p, age_grp = groups, sex = strata, population = n)
# add weights to a stratified simple random sample
# weight based on age group and sex
add_weights_strata(x, p = p, age_grp, sex, population = population)
#> cluster household_id eligibile_n surveyed_n individual_id age_grp sex
#> 1 Village A 1 6 4 1 0-10 Male
#> 2 Village A 1 6 4 2 20-30 Female
#> 3 Village A 1 6 4 3 30-40 Male
#> 4 Village A 1 6 4 4 50-60 Female
#> 5 Village A 2 6 4 4 50-60 Female
#> 6 Village B 2 3 3 1 20-30 Male
#> 7 Village B 2 3 3 2 50-60 Female
#> 8 Village B 2 3 3 3 30-40 Female
#> outcome surv_weight_ID surv_weight
#> 1 Y 0-10_Male 1000.0000
#> 2 Y 20-30_Female 3000.0000
#> 3 N 30-40_Male 4000.0000
#> 4 N 50-60_Female 333.3333
#> 5 N 50-60_Female 333.3333
#> 6 N 20-30_Male 3000.0000
#> 7 N 50-60_Female 333.3333
#> 8 Y 30-40_Female 4000.0000