calculateChauvenetCriterion: Calculate Chauvenet's criterion for outlier detection

In CeMOS-Mannheim/MALDIcellassay: Automated MALDI Cell Assays Using Dose-Response Curve Fitting

Calculate Chauvenet's criterion for outlier detection

Description

Calculate Chauvenet's criterion for outlier detection

Usage

calculateChauvenetCriterion(x)

Arguments

x	numeric, values (e.g. intensities) to test for outliers

Details

Note that, as for all outlier detection criteria: Excluding data points from your measurement should only be conducted with extreme care. Even if this (or any other) function tells you that a data point is an outlier, you might still want to have it in your sample population especially if you are not sure if your data is normal distributed. See Wikipedia for details of the algorithm.

Value

logical vector, TRUE for detected outliers.

Examples

set.seed(42) 

#no outlier
sample <- rnorm(n = 8, mean = 0, sd = 0.01)
calculateChauvenetCriterion(sample)

# introduce outlier
sample[1] <- 1
calculateChauvenetCriterion(sample)

CeMOS-Mannheim/MALDIcellassay documentation built on Jan. 24, 2025, 11:17 p.m.