The “chisq.test” Function in R

• Package: Base R (no specific package required)

• Purpose: Performs chi-squared tests of independence and goodness-of-fit.

• General Class: Statistical Testing

• Required Argument(s):

• x: A contingency table or a matrix representing the observed counts.

• Notable Optional Arguments:

• y: An optional matrix representing expected counts for a goodness-of-fit test.

• correct: A logical value indicating whether to apply a continuity correction for a 2x2 table. The default is TRUE.

• simulate.p.value: A logical value indicating whether to compute a p-value by Monte Carlo simulation. The default is FALSE.

• B: The number of replicates for Monte Carlo simulation. The default is 2000.

• expected: A logical value indicating whether to return expected counts. The default is TRUE.

• rescale.p: A logical value indicating whether to rescale p-values for Monte Carlo simulation. The default is FALSE.

• Example:

• # Example data for a chi-squared test of independence
  set.seed(123)
  obs_table <- matrix(c(25, 15, 10, 20), nrow = 2)
  
  # Perform a chi-squared test
  result <- chisq.test(obs_table)
  
  # Display the result
  print(result)

• In this example, the chisq.test function is used to perform a chi-squared test of independence on a 2x2 contingency table (obs_table). The result of the test, including the test statistic, degrees of freedom, and p-value, is then printed to the console.