acc_robust_univariate_outlier: Function to identify univariate outliers by four different...

Description Usage Arguments Value ALGORITHM OF THIS IMPLEMENTATION: See Also

Description

A classical but still popular approach to detect univariate outlier is the boxplot method introduced by Tukey 1977. The boxplot is a simple graphical tool to display information about continuous univariate data (e.g., median, lower and upper quartile). Outliers are defined as values deviating more than 1.5 \times IQR from the 1st (Q25) or 3rd (Q75) quartile. The strength of Tukey’s method is that it makes no distributional assumptions and thus is also applicable to skewed or non mound-shaped data Marsh and Seo, 2006. Nevertheless, this method tends to identify frequent measurements which are falsely interpreted as true outliers.

A somewhat more conservative approach in terms of symmetric and/or normal distributions is the 6 * σ approach, i.e. any measurement not in the interval of mean(x) +/- 3 * σ is considered an outlier.

Both methods mentioned above are not ideally suited to skewed distributions. As many biomarkers such as laboratory measurements represent in skewed distributions the methods above may be insufficient. The approach of Hubert and Vandervieren 2008 adjusts the boxplot for the skewness of the distribution. This approach is implemented in several R packages such as robustbase::mc which is used in this implementation of dataquieR.

Another completely heuristic approach is also included to identify outliers. The approach is based on the assumption that the distances between measurements of the same underlying distribution should homogeneous. For comprehension of this approach:

Usage

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acc_robust_univariate_outlier(
  resp_vars = NULL,
  label_col,
  study_data,
  meta_data,
  exclude_roles,
  n_rules = 4
)

Arguments

resp_vars

variable list the name of the continuous measurement variable

label_col

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

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

exclude_roles

variable roles a character (vector) of variable roles not included

n_rules

integer from=1 to=4. the no. of rules that must be violated to flag a variable as containing outliers. The default is 4, i.e. all.

Value

a list with:

ALGORITHM OF THIS IMPLEMENTATION:

See Also

acc_univariate_outlier


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