calculateChauvenetCriterion {MALDIcellassay} | R Documentation |
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)
[Package MALDIcellassay version 0.4.47 Index]