acc_multivariate_outlier: Function to calculate and plot Mahalanobis distances

Description Usage Arguments Value ALGORITHM OF THIS IMPLEMENTATION: See Also

View source: R/acc_multivariate_outlier.R

Description

A standard tool to detect multivariate outliers is the Mahalanobis distance. This approach is very helpful for the interpretation of the plausibility of a measurement given the value of another. In this approach the Mahalanobis distance is used as a univariate measure itself. We apply the same rules for the identification of outliers as in univariate outliers:

For further details, please see the vignette for univariate outlier.

Usage

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acc_multivariate_outlier(
  resp_vars,
  id_vars = NULL,
  label_col,
  n_rules = 4,
  study_data,
  meta_data
)

Arguments

resp_vars

variable list len=1-2. the name of the continuous measurement variable

id_vars

variable optional, an ID variable of the study data. If not specified row numbers are used.

label_col

variable attribute the name of the column in the metadata with labels of variables

n_rules

numeric from=1 to=4. the no. of rules that must be violated to classify as outlier

study_data

data.frame the data frame that contains the measurements

meta_data

data.frame the data frame that contains metadata attributes of study data

Value

a list with:

ALGORITHM OF THIS IMPLEMENTATION:

See Also

Online Documentation


dataquieR documentation built on Feb. 26, 2021, 5:08 p.m.