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ARD survey categorical CIs

Source: R/ard_categorical_ci.survey.design.R
ard_categorical_ci.survey.design.Rd

Confidence intervals for categorical variables calculated via survey::svyciprop().

Usage

# S3 method for survey.design
ard_categorical_ci(
  data,
  variables,
  by = NULL,
  method = c("logit", "likelihood", "asin", "beta", "mean", "xlogit"),
  conf.level = 0.95,
  value = list(where(is_binary) ~ 1L, where(is.logical) ~ TRUE),
  df = survey::degf(data),
  ...
)

Arguments

data

(survey.design)
a design object often created with survey::svydesign().

variables

(tidy-select)
columns to include in summaries.

by

(tidy-select)
results are calculated for all combinations of the columns specified, including unobserved combinations and unobserved factor levels.

method

(string)
Method passed to survey::svyciprop(method)

conf.level

(numeric)
a scalar in (0, 1) indicating the confidence level. Default is 0.95

value

(formula-list-selector)
function will calculate the CIs for all levels of the variables specified. Use this argument to instead request only a single level by summarized. Default is list(where(is_binary) ~ 1L, where(is.logical) ~ TRUE), where columns coded as 0/1 and TRUE/FALSE will summarize the 1 and TRUE levels.

df

(numeric)
denominator degrees of freedom, passed to survey::svyciprop(df). Default is survey::degf(data).

...

arguments passed to survey::svyciprop()

Value

ARD data frame

Examples

data(api, package = "survey")
dclus1 <- survey::svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)

ard_categorical_ci(dclus1, variables = sch.wide)
#> {cards} data frame: 10 x 9
#>    variable variable_level   context  stat_name stat_label  stat
#> 1  sch.wide             No categori…   estimate   estimate 0.126
#> 2  sch.wide             No categori…   conf.low   conf.low 0.088
#> 3  sch.wide             No categori…  conf.high  conf.high 0.176
#> 4  sch.wide             No categori…     method     method logit
#> 5  sch.wide             No categori… conf.level  conf.lev…  0.95
#> 6  sch.wide            Yes categori…   estimate   estimate 0.874
#> 7  sch.wide            Yes categori…   conf.low   conf.low 0.824
#> 8  sch.wide            Yes categori…  conf.high  conf.high 0.912
#> 9  sch.wide            Yes categori…     method     method logit
#> 10 sch.wide            Yes categori… conf.level  conf.lev…  0.95
#> ℹ 3 more variables: fmt_fn, warning, error
ard_categorical_ci(dclus1, variables = sch.wide, value = sch.wide ~ "Yes", method = "xlogit")
#> {cards} data frame: 5 x 9
#>   variable variable_level   context  stat_name stat_label   stat
#> 1 sch.wide            Yes categori…   estimate   estimate  0.874
#> 2 sch.wide            Yes categori…   conf.low   conf.low  0.824
#> 3 sch.wide            Yes categori…  conf.high  conf.high  0.912
#> 4 sch.wide            Yes categori…     method     method xlogit
#> 5 sch.wide            Yes categori… conf.level  conf.lev…   0.95
#> ℹ 3 more variables: fmt_fn, warning, error