CorrelationTests: Correlation Tests

In coin: Conditional Inference Procedures in a Permutation Test Framework

Correlation Tests

Description

Testing the independence of two numeric variables.

Usage

## S3 method for class 'formula'
spearman_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
spearman_test(object, distribution = c("asymptotic", "approximate", "none"), ...)

## S3 method for class 'formula'
fisyat_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
fisyat_test(object, distribution = c("asymptotic", "approximate", "none"),
            ties.method = c("mid-ranks", "average-scores"), ...)

## S3 method for class 'formula'
quadrant_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
quadrant_test(object, distribution = c("asymptotic", "approximate", "none"),
              mid.score = c("0", "0.5", "1"), ...)

## S3 method for class 'formula'
koziol_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
koziol_test(object, distribution = c("asymptotic", "approximate", "none"),
            ties.method = c("mid-ranks", "average-scores"), ...)

Arguments

formula	a formula of the form y ~ x | block where y and x are numeric variables and block is an optional factor for stratification.
data	an optional data frame containing the variables in the model formula.
subset	an optional vector specifying a subset of observations to be used. Defaults to NULL.
weights	an optional formula of the form ~ w defining integer valued case weights for each observation. Defaults to NULL, implying equal weight for all observations.
object	an object inheriting from class "IndependenceProblem".
distribution	a character, the conditional null distribution of the test statistic can be approximated by its asymptotic distribution ("asymptotic", default) or via Monte Carlo resampling ("approximate"). Alternatively, the functions asymptotic or approximate can be used. Computation of the null distribution can be suppressed by specifying "none".
ties.method	a character, the method used to handle ties: the score generating function either uses mid-ranks ("mid-ranks", default) or averages the scores of randomly broken ties ("average-scores").
mid.score	a character, the score assigned to observations exactly equal to the median: either 0 ("0", default), 0.5 ("0.5") or 1 ("1"); see median_test().
...	further arguments to be passed to independence_test().

Details

spearman_test(), fisyat_test(), quadrant_test() and koziol_test() provide the Spearman correlation test, the Fisher-Yates correlation test using van der Waerden scores, the quadrant test and the Koziol-Nemec test. A general description of these methods is given by \bibcitet|coin::theory-of-:-1999|Sec. 4.6. The Koziol-Nemec test was suggested by \bibcitetcoin::koziol_1979. For the adjustment of scores for tied values see \bibcitet|coin::theory-of-:-1999|pp. 133–135.

The null hypothesis of independence, or conditional independence given block, between y and x is tested.

The conditional null distribution of the test statistic is used to obtain p-values and an asymptotic approximation of the exact distribution is used by default (distribution = "asymptotic"). Alternatively, the distribution can be approximated via Monte Carlo resampling by setting distribution to "approximate". See asymptotic() and approximate() for details.

Value

An object inheriting from class "IndependenceTest".

References

\bibshow

*

Examples

## Asymptotic Spearman test
spearman_test(CONT ~ INTG, data = USJudgeRatings)

## Asymptotic Fisher-Yates test
fisyat_test(CONT ~ INTG, data = USJudgeRatings)

## Asymptotic quadrant test
quadrant_test(CONT ~ INTG, data = USJudgeRatings)

## Asymptotic Koziol-Nemec test
koziol_test(CONT ~ INTG, data = USJudgeRatings)

coin documentation built on June 30, 2026, 9:06 a.m.