Weiwei Shi wrote:
Hi, there:
I think i need to re-phrase my question since last time I did not get
any reply but i think the question is not that hard, probably i did
not make the question clear:
I want to find cases like
35, 90, 330, 330, 335
from the rest which look like
3, 3, 3, 3.2, 3.3
4, 4.4, 4.5, 4.6, 4.7
....
basically there is one (or more) big 'gap' in the case i seek.
Hi Weiwei,
I think your method of defining a central value for the large proportion
of values and then setting a criterion for outliers is valid (or at
least as valid as many other ways of defining outliers). However, here
is a different method, sorting the vector of values and then looking for
a "gap" with a specified multiple (gap.prop) of the mean differences
between the smaller values. It returns the first value after the "gap"
(easily changed to all the values after). To account for vectors that
have negative values the minimum value is subtracted when calculating
"newx" and then added to the result. For your data, a gap.prop of 20
works, but the default value of 10 doesn't. It also won't work where
large values are typical and small ones are the outliers (well, it will
indicate where the "gap" is).
Jim
find.first.gap<-function(x,gap.prop=10) {
lenx<-length(x)
newx<-sort(x)-min(x)
not.found<-1
gap.pos<-2
# set the
mean.diff<-newx[2]-newx[1]
while(not.found && gap.pos <= lenx) {
this.diff<-newx[gap.pos]-newx[gap.pos-1]
print(c(mean.diff,this.diff))
if(mean.diff != 0) {
if(this.diff/mean.diff >= gap.prop) not.found<-0
else gap.pos<-gap.pos+1
}
else gap.pos<-gap.pos+1
mean.diff<-(this.diff+mean.diff*(gap.pos-1))/gap.pos
}
return(newx[gap.pos]+min(x))
}
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