outliers (version 0.14)

# grubbs.test: Grubbs tests for one or two outliers in data sample

## Description

Performs Grubbs' test for one outlier, two outliers on one tail, or two outliers on opposite tails, in small sample.

## Usage

`grubbs.test(x, type = 10, opposite = FALSE, two.sided = FALSE)`

## Arguments

x
a numeric vector for data values.
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.)
type
Integer value indicating test variant. 10 is a test for one outlier (side is detected automatically and can be reversed by `opposite` parameter). 11 is a test for two outliers on opposite tails, 20 is test for two outliers in one tail.
two.sided
Logical value indicating if there is a need to treat this test as two-sided.

## Value

statistic
the value statistic. For type 10 it is difference between outlier and the mean divided by standard deviation, and for type 20 it is sample range divided by standard deviation. Additional value U is ratio of sample variances with and withour suspicious outlier. According to Grubbs (1950) these values for type 10 are bound by simple formula and only one of them can be used, but function gives both. For type 20 the G is the same as U.
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.

## Details

The function can perform three tests given and discussed by Grubbs (1950).

First test (10) is used to detect if the sample dataset contains one outlier, statistically different than the other values. Test is based by calculating score of this outlier G (outlier minus mean and divided by sd) and comparing it to appropriate critical values. Alternative method is calculating ratio of variances of two datasets - full dataset and dataset without outlier. The obtained value called U is bound with G by simple formula.

Second test (11) is used to check if lowest and highest value are two outliers on opposite tails of sample. It is based on calculation of ratio of range to standard deviation of the sample.

Third test (20) calculates ratio of variance of full sample and sample without two extreme observations. It is used to detect if dataset contains two outliers on the same tail.

The p-values are calculated using `qgrubbs` function.

## References

Grubbs, F.E. (1950). Sample Criteria for testing outlying observations. Ann. Math. Stat. 21, 1, 27-58.

`dixon.test`, `chisq.out.test`
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