Hi Philippe,
you could consider using the Windsorized mean,
winds.mean <- function(x, k=2){
y <- x[!is.na(x)]
mu <- mean(y)
stdev <- sd(y)
outliers.up <- y[y>mu+k*stdev]
outliers.lo <- y[y<mu-k*stdev]
y[y==outliers.up] <- mu+k*stdev
y[y==outliers.lo] <- mu-k*stdev
list(mean=sum(y)/length(y), outliers.up=outliers.up, outliers.lo=outliers.lo)
}
##################
x <- c(10,11,12,15,20,22,25,30,500) mean(x) winds.mean(x)
I hope this helps.
Best, Dimitris
---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/396887 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, September 23, 2004 4:22 PM
Subject: [R] detection of outliers
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
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