ESD_test | R Documentation |
Perform Rosner's generalized extreme Studentized deviate (ESD) test, which assumes that the distribution is normal (Gaussian), can be used when the number of outliers is unknown, and becomes more robust as the number of samples increases.
ESD_test(x, alpha = 0.05, h = 5)
x |
( |
alpha |
( |
h |
( |
A list class containing the results of the ESD test.
stat
a data frame contains the several statistics about ESD test that includes
the index(i
), Mean, SD, raw data(x
), the location(Obs
) in x
, ESD statistics(ESDi),
Lambda and Outliers(TRUE
or FALSE
).
ord
a vector with the order index of outliers that is equal to Obs
in
the stat
data frame.
The algorithm for determining the number of outliers is as follows:
Compare ESDi with Lambda. If ESDi > Lambda then the observations will be regards as outliers.
The order index corresponds to the available x
data that has been removed the
missing (NA) value.
As we should compare if the ESD(h) and ESD(h+1) are equal, the h+1 ESD values will be shown. If they are identical, both of them can not be regarded as outliers.
CLSI EP09A3 Appendix B. Detecting Aberrant Results (Outliers).
data("platelet")
res <- blandAltman(x = platelet$Comparative, y = platelet$Candidate)
ESD_test(x = res@stat$relative_diff)
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