chisq.out.test: Chi-squared test for outlier

In outliers: Tests for Outliers

Chi-squared test for outlier

Description

Performs a chisquared test for detection of one outlier in a vector.

Usage

chisq.out.test(x, variance=var(x), opposite = FALSE)

Arguments

x	a numeric vector for data values.
variance	known variance of population. if not given, estimator from sample is taken, but there is not so much sense in such test (it is similar to z-scores)
opposite	a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.)

Details

This function performs a simple test for one outlier, based on chisquared distribution of squared differences between data and sample mean. It assumes known variance of population. It is rather not recommended today for routine use, because several more powerful tests are implemented (see other functions mentioned below). It was discussed by Dixon (1950) for the first time, as one of the tests taken into account by him.

Value

A list with class htest containing the following components:

statistic	the value of chisquared-statistic.
p.value	the p-value for the test.
alternative	a character string describing the alternative hypothesis.
method	a character string indicating what type of test was performed.
data.name	name of the data argument.

Note

This test is known to reject only extreme outliers, if no known variance is specified.

Author(s)

Lukasz Komsta

References

Dixon, W.J. (1950). Analysis of extreme values. Ann. Math. Stat. 21, 4, 488-506.

See Also

dixon.test, grubbs.test

Examples

set.seed(1234)
x = rnorm(10)
chisq.out.test(x)
chisq.out.test(x,opposite=TRUE)

outliers documentation built on March 26, 2022, 9:05 a.m.