# outliers: Tests for outliers

``````> library(outliers)
``````

バージョン: 0.14

`chisq.out.test` Chi-squared test for outlier
`cochran.test` Test for outlying or inlying variance
`dixon.test` Dixon tests for outlier
`grubbs.test` Grubbs tests for one or two outliers in data sample
`outlier` Find value with largest difference from the mean
`qcochran` Critical values and p-values for Cochran outlying variance test
`qdixon` critical values and p-values for Dixon tests
`qgrubbs` Calculate critical values and p-values for Grubbs tests
`qtable` Interpolate tabularized distribution
`rm.outlier` Remove the value(s) most differing from the mean
`scores` Calculate scores of the sample

## chisq.out.test

``````> set.seed(71)
> x <- rnorm(100)
> chisq.out.test(x)
``````
``````
chi-squared test for outlier

data:  x
X-squared = 8.9672, p-value = 0.002749
alternative hypothesis: highest value 3.12588239182329 is an outlier
``````
``````> chisq.out.test(x, opposite = TRUE)
``````
``````
chi-squared test for outlier

data:  x
X-squared = 4.3196, p-value = 0.03768
alternative hypothesis: lowest value -2.20359620952698 is an outlier
``````
``````> # boxplot(x)
``````

## grubbs.test

### Arguments

• x
• opposite
• type
• two.sided
``````> set.seed(1234)
> x = rnorm(10)
> grubbs.test(x)
``````
``````
Grubbs test for one outlier

data:  x
G = 1.97080, U = 0.52047, p-value = 0.1323
alternative hypothesis: lowest value -2.34569770262935 is an outlier
``````
``````> grubbs.test(x, type = 20)
``````
``````
Grubbs test for two outliers

data:  x
U = 0.3836, p-value = 0.2459
alternative hypothesis: lowest values -2.34569770262935 , -1.20706574938542 are outliers
``````
``````> grubbs.test(x, type = 11)
``````
``````
Grubbs test for two opposite outliers

data:  x
G = 3.44460, U = 0.32364, p-value = 0.195
alternative hypothesis: -2.34569770262935 and 1.08444117668306 are outliers
``````

## outlier

（平均からの）外れ値の検出

### Arguments

• x
• opposite
• logical
``````> set.seed(1234)
> y <- rnorm(100)
> outlier(y)
``````
``````[1] 2.548991
``````
``````> outlier(y, opposite = TRUE)
``````
``````[1] -2.345698
``````

## rm.outlier

``````> y %>% length()
``````
``````[1] 100
``````
``````> rm.outlier(y) %>% length()
``````
``````[1] 99
``````