If you look at ?boxplot.stats, you will find that the confidence interval it
reports is centred on the median and :
"The notches (if requested) extend to '+/-1.58 IQR/sqrt(n)'."
If you have skewed data it is very possible (as you have found) that the mean
is outside median+/-1.58 IQR/sqrt(n).
All that is happening is that the majority of the data are around 1 or 2 and
you have a substantial number near zero. Result: mean much lower than median.
And with a high n, the boxplot notch is very narrow and excludes the mean.
But it does sound very much as if you are doing something questionable at best.
I would not trust IQR as a dispersion measure on discrete data with few
possible values even if they were on an interval scale; too much risk of
getting the same IQR for many different distributions. On an ordinal scale it
is worse; the only points that are valid at all are the valid scale values, so
a CI that uses intermediate values is formally meaningless (what is a shoe size
of 7.2, for example? Answer: Not a shoe size at all). It is of course entirely
meaningless to talk about an IQR on a categorical scale.
It sounds like boxplot.stats is an inappropriate tool for summarising your data.
>>> Tom Willems <[EMAIL PROTECTED]> 30/08/2007 10:00:50 >>>
Dear R ussers,
My question is, " How can my mean be outside the confidence intervals ?!"
I think i have the answer for it, but i would like to hear some other
ideas on it.
First my data is not continuose but categorical, it is a titre calculated
on a dilution serie.
It is stored as a column of values, and a column indicating the phase of
the trail.
Theoreticaly it is possible to have a value ranging from 0 to 4, but in
practice, only sertain values will occure, and they will repeat.
So it are frequencies.
This is why i belief that it is better to work with a median than with a
mean, because it represents the cluster of values wich occure most.
Below I only give one example, but the mean being below the lowest
confidence limit occures several times over different tests.
does my answer seam reasonable, or should i perhapes use an other methode,
any sugestion?
summary_1d = summary(subset(eda_data, phase=='1' & test=='test
1' ,select=lg_value), na.rm = T)
conf_1d = boxplot.stats(subset(eda_data, phase=='1' &
test=='test 1' ,select=lg_value))
Mean Median 95% Confidence Int. StDev.
Variance
1.198 1.681 1.441 > < 1.922 0.931
0.866
Kind regards,
Tom W.
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