On May 2, 2010, at 5:27 PM, Jessica Schedlbauer wrote:
Hello,
I have a matrix in which two variables, x and y, are used together
to determine z. The variables x and y are sorted into classes.
Specifically, values for variable x range from 0 to 2.7 and are
sorted into class increments of 0.15 and variable y ranges from
0-2100 with class increments of 100. For each x-y class
combination, I have calculated a mean value of z from existing
data. However there are gaps in the existing data (i.e., x-y
combinations for which there are no z data). I would like to
interpolate values to fill in these missing datapoints, but have so
far been unable to find a straightforward way of doing so in R.
There are many gaps in the data and I need to repeat this process
for several individual datasets. That said, I am looking for a way
to avoid interpolating each missing point separately.
Why not do a loess fit _before_ you do any binning and then
predict.loess at the intervals where you want estimates?
x=rnorm(200, 1.5)
y= (10*x)^2 +rnorm(200, 0, 100)
plot(x,y, cex=0.5, xlim=c(-2, 5))
points(seq(-2,5, by=0.15), predict(loess(y~x),
newdata=data.frame(x=seq(-2,5, by=0.15))),
col="red", cex=2)
Any suggestions would be greatly appreciated.
Regards,
Jessica Schedlbauer
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David Winsemius, MD
West Hartford, CT
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