<Phguardiol <at> aol.com> writes:
:
: Hi,
: this is both a statistical and a R question...
: what would the best way / test to detect an outlier value among a series of
10 to 30 values ? for instance if we
: have the following dataset: 10,11,12,15,20,22,25,30,500 I d like to have a
way to identify the last data
: as an outlier (only one direction). One way would be to calculate abs(mean -
median) and if elevated (to
: what extent ?) delete the extreme data then redo.. but is it valid to do so
with so few data ? is the (trimmed
: mean - mean) more efficient ? if so, what would be the maximal tolerable
value to use as a threshold ? (I guess
: it will be experiment dependent...) tests for skweness will probably
required a larger dataset ?
: any suggestions are very welcome !
: thanks for your help
: Philippe Guardiola, MD
If z is your vector the following all detect outliers:
boxplot(z) # will show the outlier
plot(lm(z ~ 1)) # the various plots show this as well
require(car)
outlier.test(lm(z ~ 1)) # tests most extreme value
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