Skip to contents

ARD Proportion Confidence Intervals

Source: R/ard_categorical_ci.R
ard_categorical_ci.Rd

Calculate confidence intervals for proportions.

Usage 

ard_categorical_ci(data, ...)

# S3 method for class 'data.frame'
ard_categorical_ci(
  data,
  variables,
  by = dplyr::group_vars(data),
  method = c("waldcc", "wald", "clopper-pearson", "wilson", "wilsoncc", "strat_wilson",
    "strat_wilsoncc", "agresti-coull", "jeffreys"),
  denominator = c("column", "row", "cell"),
  conf.level = 0.95,
  value = list(where(is_binary) ~ 1L, where(is.logical) ~ TRUE),
  strata = NULL,
  weights = NULL,
  max.iterations = 10,
  ...
)

Arguments

data

(data.frame)
a data frame

...

Arguments passed to methods.

variables

(tidy-select)
columns to include in summaries. Columns must be class <logical> or <numeric> values coded as c(0,1).

by

(tidy-select)
columns to stratify calculations by.

method

(string)
string indicating the type of confidence interval to calculate. Must be one of . See ?proportion_ci for details.

denominator

(string)
Must be one of 'column' (default), 'row', and 'cell', which specifies the direction of the calculation/denominator. Argument is similar to cards::ard_tabulate(denominator).

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.

strata, weights, max.iterations

arguments passed to proportion_ci_strat_wilson(), when method='strat_wilson'

Value

an ARD data frame

Examples 

# compute CI for binary variables
ard_categorical_ci(mtcars, variables = c(vs, am), method = "wilson")
#> # An ARD data frame: 22 × 9
#>    variable variable_level stat_name 
#>    <chr>            <list> <chr>     
#>  1 vs                    1 N         
#>  2 vs                    1 n         
#>  3 vs                    1 conf.level
#>  4 vs                    1 estimate  
#>  5 vs                    1 statistic 
#>  6 vs                    1 p.value   
#>  7 vs                    1 parameter 
#>  8 vs                    1 conf.low  
#>  9 vs                    1 conf.high 
#> 10 vs                    1 method    
#> # ℹ 12 more rows
#> # ℹ 6 more variables: context <chr>, stat_label <chr>, stat <list>,
#> #   fmt_fun <list>, warning <list>, error <list>

# compute CIs for each level of a categorical variable
ard_categorical_ci(mtcars, variables = cyl, method = "jeffreys")
#> # An ARD data frame: 21 × 9
#>    variable variable_level context       stat_name  stat_label stat             
#>    <chr>            <list> <chr>         <chr>      <chr>      <list>           
#>  1 cyl                   4 proportion_ci N          N          32               
#>  2 cyl                   4 proportion_ci n          n          11               
#>  3 cyl                   4 proportion_ci estimate   estimate   0.34375          
#>  4 cyl                   4 proportion_ci conf.low   conf.low   0.1982694        
#>  5 cyl                   4 proportion_ci conf.high  conf.high  0.5160952        
#>  6 cyl                   4 proportion_ci conf.level conf.level 0.95             
#>  7 cyl                   4 proportion_ci method     method     Jeffreys Interval
#>  8 cyl                   6 proportion_ci N          N          32               
#>  9 cyl                   6 proportion_ci n          n          7                
#> 10 cyl                   6 proportion_ci estimate   estimate   0.21875          
#> # ℹ 11 more rows
#> # ℹ 3 more variables: fmt_fun <list>, warning <list>, error <list>