ARD survey categorical CIs — ard_categorical

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

Usage

# S3 method for class '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: (scalar 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)
#> # An ARD data frame: 10 × 9
#>    variable variable_level context       stat_name stat_label stat       fmt_fun
#>    <chr>    <list>         <chr>         <chr>     <chr>      <list>     <list> 
#>  1 sch.wide No             categorical_… estimate  estimate   0.1256831  2      
#>  2 sch.wide No             categorical_… conf.low  conf.low   0.08809992 2      
#>  3 sch.wide No             categorical_… conf.high conf.high  0.1762011  2      
#>  4 sch.wide No             categorical_… method    method     logit      <fn>   
#>  5 sch.wide No             categorical_… conf.lev… conf.level 0.95       2      
#>  6 sch.wide Yes            categorical_… estimate  estimate   0.8743169  2      
#>  7 sch.wide Yes            categorical_… conf.low  conf.low   0.8237989  2      
#>  8 sch.wide Yes            categorical_… conf.high conf.high  0.9119001  2      
#>  9 sch.wide Yes            categorical_… method    method     logit      <fn>   
#> 10 sch.wide Yes            categorical_… conf.lev… conf.level 0.95       2      
#> # ℹ 2 more variables: warning <list>, error <list>
ard_categorical_ci(dclus1, variables = sch.wide, value = sch.wide ~ "Yes", method = "xlogit")
#> # An ARD data frame: 5 × 9
#>   variable variable_level context        stat_name  stat_label stat      fmt_fun
#>   <chr>    <list>         <chr>          <chr>      <chr>      <list>    <list> 
#> 1 sch.wide Yes            categorical_ci estimate   estimate   0.8743169 2      
#> 2 sch.wide Yes            categorical_ci conf.low   conf.low   0.8237989 2      
#> 3 sch.wide Yes            categorical_ci conf.high  conf.high  0.9119001 2      
#> 4 sch.wide Yes            categorical_ci method     method     xlogit    <fn>   
#> 5 sch.wide Yes            categorical_ci conf.level conf.level 0.95      2      
#> # ℹ 2 more variables: warning <list>, error <list>