Create Strata from Multiple Variables

This function creates strata (intersectional categories) from multiple categorical variables in a dataset.

Usage

make_strata(
  data,
  vars,
  sep = " × ",
  min_n = 1,
  autobin = TRUE,
  bin_rows = NULL
)

Arguments

data

A data frame containing the variables to create strata from.

vars

Character vector of variable names to use for creating strata.

sep

Separator to use between variable values when creating stratum labels. Default is " \u00d7 " (a mathematical multiplication sign).

min_n

Minimum number of observations required for a stratum to be included. Strata with fewer observations will be coded as NA. Default is 1.

autobin

Logical indicating whether to automatically bin numeric grouping variables with more than 10 unique values into 3 categories (tertiles). Default is TRUE. When this happens a message() is emitted, because the resulting strata are data-dependent (tertile cut-points depend on the sample) and a continuous variable placed in the grouping term is usually unintended. Set autobin = FALSE to disable, or bin the variable yourself for explicit, reproducible cut-points.

bin_rows

Optional logical vector, one element per row of data, selecting the rows used to compute the auto-bin cut-points (every row is still assigned a stratum). Mainly for internal use by fit_maihda, which passes the subset rows so that fit_maihda(..., subset = keep) bins on the same sample as fitting data[keep, ]. Default NULL uses all rows.

Value

An object of class maihda_strata: a list with elements

data

The original data frame with an added 'stratum' column. The strata_info is also attached as an attribute for use by fit_maihda()

strata_info

A data frame with information about each stratum including counts and the combination of variable values

vars

The stratum-defining variable names, as supplied.

sep

The label separator used.

min_n

The minimum stratum size applied.

autobin_info

A named list with the breaks and labels of each auto-binned numeric variable (empty when nothing was binned).

Details

If any of the specified variables has a missing value (NA) for a given observation, that observation will be assigned to the NA stratum (stratum = NA), rather than creating a stratum that includes the missing value.

The strata_info data frame is also attached as an attribute to the data, which allows fit_maihda() to automatically capture stratum labels for use in plots and summaries.

When autobin discretises a numeric grouping variable v, the adjusted-model and prediction machinery later add an internal factor column named .maihda_dim_<v>; the .maihda_dim_ prefix is therefore reserved. make_strata() errors if data already holds the .maihda_dim_<v> column for a variable it is about to auto-bin, so an existing user column is never silently overwritten (rename it, or pass autobin = FALSE).

A numeric grouping variable with 10 or fewer unique values (or any numeric when autobin = FALSE) is kept as its raw values: distinct values still define distinct strata, but the variable is not binned. If such a variable is then used to build an adjusted MAIHDA (maihda), it enters the adjusted model as a linear fixed effect rather than categorical main effects, and maihda() warns (when it has three or more distinct values). Wrap category codes in factor() before creating strata if you want them treated categorically.

Examples

# Create strata from gender and race variables
result <- make_strata(maihda_sim_data, vars = c("gender", "race"))
print(result$strata_info)
#>   stratum             label   n gender     race
#> 1       1      Male × White 152   Male    White
#> 2       2    Female × White 150 Female    White
#> 3       3   Male × Hispanic  52   Male Hispanic
#> 4       4 Female × Hispanic  32 Female Hispanic
#> 5       5    Female × Black  50 Female    Black
#> 6       6      Male × Black  44   Male    Black
#> 7       7      Male × Asian  13   Male    Asian
#> 8       8    Female × Asian   7 Female    Asian