scf (version 1.0.4)

scf_freq: Tabulate a Discrete Variable from SCF Microdata

Description

This function estimates the relative frequency (proportion) of each category in a discrete variable from the SCF public-use microdata. Use this function to discern the univariate distribution of a discrete variable.

Usage

scf_freq(scf, var, by = NULL, percent = TRUE)

Value

A list of class "scf_freq" with:

results

Pooled category proportions and standard errors, by group if specified.

imps

A named list of implicate-level proportion estimates.

aux

Metadata about the variable and grouping structure.

Arguments

scf

A scf_mi_survey object created by scf_load(). Must contain five replicate-weighted implicates.

var

A one-sided formula specifying a categorical variable (e.g., ~racecl).

by

Optional one-sided formula specifying a discrete grouping variable (e.g., ~own).

percent

Logical. If TRUE (default), scales results and standard errors to percentages.

Details

Proportions are estimated within each implicate using survey::svymean(), then pooled using the standard MI formula for proportions. When a grouping variable is provided via by, estimates are produced separately for each group-category combination. Results may be scaled to percentages using the percent argument.

Estimates are pooled using the standard formula:

• The mean of implicate-level proportions is the point estimate

• The standard error reflects both within-implicate variance and across-implicate variation

Unlike means or model parameters, category proportions do not use Rubin's full combination rules (e.g., degrees of freedom).

See Also

scf_xtab(), scf_plot_dist()]

Examples

# Do not implement these lines in real analysis:
# Use functions `scf_download()` and `scf_load()`
td  <- tempdir()
src <- system.file("extdata", "scf2022_mock_raw.rds", package = "scf")
file.copy(src, file.path(td, "scf2022.rds"), overwrite = TRUE)
scf2022 <- scf_load(2022, data_directory = td)

# Example for real analysis: Proportions of homeownership
scf_freq(scf2022, ~own)

# Example for real analysis: ross-tabulate education by homeownership
scf_freq(scf2022, ~own, by = ~edcl)

# Do not implement these lines in real analysis: Cleanup for package check
unlink("scf2022.rds", force = TRUE)