On Thu, Nov 21, 2013 at 8:35 AM, Trevor Walker
trevordaviswal...@gmail.comwrote:
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
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
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 -
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
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