outliers_mad {Routliers}

MAD function to detect outliers

Description

Detecting univariate outliers using the robust median absolute deviation

Usage

outliers_mad(x, b, threshold, na.rm)

Arguments

x

vector of values from which we want to compute outliers

b

constant depending on the assumed distribution underlying the data, that equals 1/Q(0.75). When the normal distribution is assumed, the constant 1.4826 is used (and it makes the MAD and SD of normal distributions comparable).

threshold

the number of MAD considered as a threshold to consider a value an outlier

na.rm

set whether Missing Values should be excluded (na.rm = TRUE) or not (na.rm = FALSE) - defaults to TRUE

Value

Returns Call, median, MAD, limits of acceptable range of values, number of outliers

Examples

#### Run outliers_mad
x <- runif(150,-100,100)
outliers_mad(x, b = 1.4826,threshold = 3,na.rm = TRUE)

#### Results can be stored in an object.
data(Intention)
res1=outliers_mad(Intention$age)
# Moreover, a list of elements can be extracted from the function,
# such as all the extremely high values,
# That will be sorted in ascending order
#### The function should be performed on dimension rather than on isolated items
data(Attacks)
SOC <- rowMeans(Attacks[,c("soc1r","soc2r","soc3r","soc4","soc5","soc6",
"soc7r","soc8","soc9","soc10r","soc11","soc12","soc13")])
res=outliers_mad(x = SOC)