On Thu, Nov 21, 2013 at 8:35 AM, Trevor Walker
wrote:
> I often work with tree data that is sampled with probability proportional
> to size, which presents a special challenge when describing the frequency
> distribution.
The survey package does lots of calculations (including quantiles) for
un
You could try:
require(quantreg)
qs <- rq(x ~ 1, weights = w, tau = 1:3/4)
Roger Koenker
rkoen...@illinois.edu
On Nov 20, 2013, at 4:56 PM, David Winsemius wrote:
>
> On Nov 20, 2013, at 11:35 AM, Trevor Walker wrote:
>
>> I often work with tree data that is sampled with probability propo
On Nov 20, 2013, at 11:35 AM, Trevor Walker wrote:
> I often work with tree data that is sampled with probability proportional
> to size, which presents a special challenge when describing the frequency
> distribution. For example, R functions like quantile() and fitdistr()
> expect each observa
Rather than "exploding", I suggest you order your data according to tree
diameter, then calculate the cumulative sum of the tree densities, and use
linear interpolation to estimate the percentiles. For example ...
library(plotrix)
attach(trees.df)
ord <- order(Diameter)
CumDensOrdScaled <- resc
I often work with tree data that is sampled with probability proportional
to size, which presents a special challenge when describing the frequency
distribution. For example, R functions like quantile() and fitdistr()
expect each observation to have equal sample probability. As a workaround,
I ha
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