I am just starting to use R, converting over from SAS. I want to estimate
the upper quartile in a scatterplot of demographic rates against rainfall in
a small mammal population. The data looks like this:
|
|* * <- point in question
|*
|* *
|* *
|* * *
|* * * * * *
|_______________________________________
Based on a paper by Scharf et al. in Ecology (vol 79, p448) I am trying to
use the qreg package in R to estimate the upper quartile in the data. My
problem is that I have relatively few data points (~40) which are mostly
distributed at the low end of the x axis, and an outlier in the y direction
at the high end of the x axis.
I would like to find a way either by iteratively weighting the observations
or by bootstrapping the regression to quantify the relationship in the mass
of the data. I looked in the manual for qreg, and while it allows for
weights, there is no guidance for routines to calculate them. I also
checked the help archives and on the web, and while I found a bit on the
web, I didnt see much that was specific to my problem. It seems like this
would be a general issue when working with naturally occurring phenomena
(like rainfall) as there will often be few data points at the extremes.
Any suggestions would be most welcome.
Thanks,
Chris
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