Convenience function that runs a chi-square test of association/independence. This is a wrapper function intended to be used for pedagogical purposes only.
associationTest(formula, data = NULL)One-sided formula specifying the two variables (required).
Optional data frame containing the variables.
An object of class 'assocTest'. When printed, the output is organised into six short sections. The first section lists the name of the test and the variables included. The second lists the null and alternative hypotheses for the test. The third shows the observed contingency table, and the fourth shows the expected contingency table under the null. The fifth prints out the test results, and the sixth reports an estimate of effect size.
The associationTest function runs the chi-square test
of association on the variables specified in the formula argument.
The formula must be a one-sided formula of the form
~variable1 + variable2, and both variables must be factors.
df <- data.frame(
gender=factor(c("male","male","male","male","female","female","female")),
answer=factor(c("heads","heads","heads","heads","tails","tails","heads"))
)
associationTest( ~ gender + answer, df )
#> Warning: Expected frequencies too small: chi-squared approximation may be incorrect
#>
#> Chi-square test of categorical association
#>
#> Variables: gender, answer
#>
#> Hypotheses:
#> null: variables are independent of one another
#> alternative: some contingency exists between variables
#>
#> Observed contingency table:
#> answer
#> gender heads tails
#> female 1 2
#> male 4 0
#>
#> Expected contingency table under the null hypothesis:
#> answer
#> gender heads tails
#> female 2.14 0.857
#> male 2.86 1.143
#>
#> Test results:
#> X-squared statistic: 1.181
#> degrees of freedom: 1
#> p-value: 0.277
#>
#> Other information:
#> estimated effect size (Cramer's v): 0.411
#> Yates' continuity correction has been applied
#> warning: expected frequencies too small, results may be inaccurate
#>