minor clarification in docstring

Author: DominiqueMakowski

R/check_outliers.R

@@ -44,7 +44,7 @@
 #' \subsection{Univariate methods}{
 #' \itemize{
 #' \item \strong{Z-scores}:
-#'  The Z-score, or standard score, is a way of describing a data point as deviance from a central value, in terms of standard deviations from the mean or, as it is here the case by default (Iglewicz, 1993), in terms of Median Absolute Deviation (MAD) from the median (which are robust measures of dispersion and centrality). The default threshold to classify outliers is 1.959, corresponding to the 2.5\% most extreme observations (assuming the data is normally distributed). Importantly, the Z-score method is univariate: it is computed column by column. If a data.frame is passed, then the maximum distance is kept for each observations. Thus, all observations that are extreme for at least one variable will be detected. However, this method is not suited for high dimensional data (with many columns), returning too liberal results (detecting many outliers).
+#'  The Z-score, or standard score, is a way of describing a data point as deviance from a central value, in terms of standard deviations from the mean or, as it is here the case by default (Iglewicz, 1993), in terms of Median Absolute Deviation (MAD) from the median (which are robust measures of dispersion and centrality). The default threshold to classify outliers is 1.959 (\code{threshold = list("zscore" = = 1.959)}), corresponding to the 2.5\% (\code{qnorm(0.975)}) most extreme observations (assuming the data is normally distributed). Importantly, the Z-score method is univariate: it is computed column by column. If a data.frame is passed, then the maximum distance is kept for each observations. Thus, all observations that are extreme for at least one variable will be detected. However, this method is not suited for high dimensional data (with many columns), returning too liberal results (detecting many outliers).
 #'
 #' \item \strong{IQR}:
 #'  Using the IQR (interquartile range) is a robust method developed by John Tukey, which often appears in box-and-whisker plots (e.g., in \code{geom_boxplot}). The interquartile range is the range between the first and the third quartiles. Tukey considered as outliers any data point that fell outside of either 1.5 times (the default threshold) the IQR below the first or above the third quartile. Similar to the Z-score method, this is a univariate method for outliers detection, returning outliers detected for at least one column, and might thus not be suited to high dimensional data.