freq_test: Hypothesis Testing for Frequency Tables

In freqtables: Make Quick Descriptive Tables for Categorical Variables

Hypothesis Testing for Frequency Tables

Description

The freq_test function is an S3 generic. It currently has methods for conducting hypothesis tests on one-way and two-way frequency tables. Further, it is made to work in a dplyr pipeline with the freq_table function.

For the freq_table_two_way class, the methods used are Pearson's chi-square test of independence Fisher's exact test. When cell counts are <= 5, Fisher's Exact Test is considered more reliable.

Usage

freq_test(.data, ...)

## S3 method for class 'freq_table_one_way'
freq_test(.data, ...)

## S3 method for class 'freq_table_two_way'
freq_test(.data, ...)

Arguments

.data	A tibble of class freq_table_one_way or freq_table_two_way.
...	Other parameters to be passed on.
method	Options for this parameter control the method used to calculate p-values.

Value

A tibble.

Examples

library(dplyr)
library(freqtables)

data(mtcars)

# Test equality of proportions

mtcars %>%
  freq_table(am) %>%
  freq_test() %>%
  select(var:percent, p_chi2_pearson)

#>  # A tibble: 2 x 6
#>      var   cat     n n_total percent p_chi2_pearson
#>    <chr> <dbl> <int>   <int>   <dbl>          <dbl>
#>  1    am     0    19      32   59.38      0.2888444
#>  2    am     1    13      32   40.62      0.2888444

# Chi-square test of independence

mtcars %>%
  freq_table(am, vs) %>%
  freq_test() %>%
  select(row_var:n, percent_row, p_chi2_pearson)

#> # A tibble: 4 x 7
#>   row_var row_cat col_var col_cat     n percent_row p_chi2_pearson
#>     <chr>   <dbl>   <chr>   <dbl> <int>       <dbl>          <dbl>
#> 1      am       0      vs       0    12       63.16      0.3409429
#> 2      am       0      vs       1     7       36.84      0.3409429
#> 3      am       1      vs       0     6       46.15      0.3409429
#> 4      am       1      vs       1     7       53.85      0.3409429

freqtables documentation built on April 3, 2022, 5:11 p.m.