Identify outliers using Z-score cut-off method.

outliersZ is used to identify outliers in vectors using Z-score cut-off

outliersZ(x, zCutOff = 1.96, replaceOutliersWith = NA,
outlierIndices = FALSE, showZValues = FALSE, digits = 2)

Arguments

x	a vector of numbers
zCutOff	value to use as cutoff (1.96 is a common value)
replaceOutliersWith	if value is an outlier, what to replace it with? NA by default
outlierIndices	return index/position of outlier
showZValues	if TRUE, will show z score of each value
digits	how many digits/decimals to round output to

Value

A vector with outliers identified (default converts outliers to NA)

Note

This detection method is not as robust as the median absolute deviation outlier detection method.

See also

outliersMAD

Author

Hause Lin

Examples

example <- c(1, 3, 3, 6, 8, 10, 10, 1000) # 1000 is an outlier
outliersZ(example)
#> 1 outliers detected.
#> Outliers replaced with NA
#> [1]  1  3  3  6  8 10 10 NA
outliersZ(example, zCutOff = 3.0)
#> 0 outliers detected.
#> Outliers replaced with NA
#> [1]    1    3    3    6    8   10   10 1000
outliersZ(example, zCutOff = 1.0, replaceOutliersWith = -999)
#> 1 outliers detected.
#> Outliers replaced with -999
#> [1]    1    3    3    6    8   10   10 -999
outliersZ(example, zCutOff = 1.0, outlierIndices = TRUE)
#> Showing indices of outliers.
#> [1] 8
outliersZ(example, zCutOff = 1.0, showZValues = TRUE)
#> Showing absolute z-scores for each value.
#> 1 outliers detected.
#> [1] -0.39 -0.39 -0.39 -0.38 -0.37 -0.37 -0.37  2.65