fast.chisq.test: Fast Zero-Tolerant Pearson's Chi-squared Test of Association

In Upsilon: Another Test of Association for Count Data

Fast Zero-Tolerant Pearson's Chi-squared Test of Association

Description

Performs a fast zero-tolerant Pearson's chi-squared test \insertCitepearson1900Upsilon to evaluate association between observations from two categorical variables.

Usage

fast.chisq.test(x, y, log.p = FALSE)

Arguments

x	a vector to specify observations of the first categorical variable. The vector can be of numeric, character, or logical type. NA values must be removed or replaced before calling the function.
y	a vector to specify observations of the second categorical variable. Must not contain NA values and must be of the same length as x.
log.p	a logical. If TRUE, the p-value is calculated in closed form to natural logarithm of p-value to improve numerical precision when p-value approaches zero. Defaults to FALSE.

Value

A list with class "htest" containing the following components:

statistic	the value of chi-squared test statistic.
parameter	the degrees of freedom.
p.value	the p-value of the test.
estimate	Cramér's V statistic representing the effect size.
method	a character string indicating the method used.
data.name	a character string giving the names of input data.

Note

The test uses an internal hash table, instead of matrix, to store the contingency table. Savings in both runtime and memory saving can be substantial if the contingency table is sparse and large. The test is implemented in C++, to give an additional layer of speedup over an R implementation.

References

\insertRef

pearson1900Upsilon

Examples

library("Upsilon")
weather <- c(
  "rainy", "sunny", "rainy", "sunny", "rainy"
)
mood <- c(
  "wistful", "upbeat", "upbeat", "upbeat", "wistful"
)

fast.chisq.test(weather, mood)

# The result is equivalent to: 
modified.chisq.test(table(weather, mood))

Upsilon documentation built on March 7, 2026, 5:07 p.m.