Skip to contents

Tidy a Survey GLM Fit

Source: R/glm-clean.R
clean.Rd

Converts a survey_glm_fit object into a survey_glm_tidy result tibble with one row per model coefficient (plus optional reference rows for factor predictors), design-based standard errors, confidence intervals, and structured metadata.

Usage

clean(
  model,
  conf_level = 0.95,
  include_reference = TRUE,
  n = FALSE,
  statistic = TRUE,
  exponentiate = FALSE,
  interaction_sep = " * ",
  ...
)

Arguments

model

A survey_glm_fit object from survey_glm().

conf_level

Numeric scalar in (0, 1). Confidence level for confidence intervals. Default 0.95.

include_reference

Logical. If TRUE, reference levels for unordered factor predictors appear as rows with estimate = NA and reference_row = TRUE. Default TRUE.

n

Logical. If TRUE, adds an n_obs column with the unweighted observation count per term. Default FALSE.

statistic

Logical. If TRUE (default), includes the statistic (t-statistic) column. Set to FALSE to drop it.

exponentiate

Logical. If TRUE, exponentiates estimate, conf_low, and conf_high. std_error is left on the log scale (matching broom convention). Fires surveycore_warning_exponentiate_nonlog when the model link is not log-based. Default FALSE.

interaction_sep

Character scalar. Separator for interaction term labels. Default " * ".

...

Currently unused.

Value

A survey_glm_tidy object: a tibble with S3 class c("survey_glm_tidy", "survey_result", "tbl_df", "tbl", "data.frame"). Metadata is accessed via meta().

See also

survey_glm() to fit the model, meta() to access metadata.

Other analysis: get_corr(), get_diffs(), get_freqs(), get_means(), get_quantiles(), get_ratios(), get_totals(), meta()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
fit <- survey_glm(d, age ~ sex)
clean(fit)
#> # A tibble: 2 × 11
#>   term       variable var_label label reference_row estimate std_error statistic
#> * <chr>      <chr>    <chr>     <chr> <lgl>            <dbl>     <dbl>     <dbl>
#> 1 (Intercep… (Interc… NA        (Int… FALSE           47.6       1.54     30.8  
#> 2 sex        sex      responde… resp… FALSE            0.260     0.957     0.271
#> # ℹ 3 more variables: p_value <dbl>, conf_low <dbl>, conf_high <dbl>
clean(fit, conf_level = 0.99, exponentiate = FALSE)
#> # A tibble: 2 × 11
#>   term       variable var_label label reference_row estimate std_error statistic
#> * <chr>      <chr>    <chr>     <chr> <lgl>            <dbl>     <dbl>     <dbl>
#> 1 (Intercep… (Interc… NA        (Int… FALSE           47.6       1.54     30.8  
#> 2 sex        sex      responde… resp… FALSE            0.260     0.957     0.271
#> # ℹ 3 more variables: p_value <dbl>, conf_low <dbl>, conf_high <dbl>