adamethods (version 1.2.1)

do_clean: Cleaning outliers

Description

Cleaning of the most remarkable outliers. This improves the performance of the archetypoid algorithm since it is not affected by spurious points.

Usage

do_clean(data, num_pts, range = 1.5, out_perc = 80)

Arguments

data

Data frame with (temporal) points in the rows and observations in the columns.

num_pts

Number of temporal points.

range

Same parameter as in function boxplot. A value of 1.5 is enough to detect amplitude and shift outliers, while a value of 3 is needed to detect isolated outliers.

out_perc

Minimum number of temporal points (in percentage) to consider the observation as an outlier. Needed when range=1.5.

Value

Numeric vector with the outliers.

See Also

boxplot

Examples

# NOT RUN {
data(mtcars)
data <- mtcars
num_pts <- ncol(data)
do_clean(t(data), num_pts, 1.5, 80)

# }