On 2012-05-17 08:40, DWatts wrote:
David Winsemius wrote
and provide commented, minimal, self-contained, reproducible code.
Still untested in absence of data.
Thank you, and my apologies for not giving some sample numbers. Below is a
short subset of the data set I'm working with.
Cfl=c(2.61,4.21,2,2.75,7.47,1.2,3.24,12.49,2.37,3.28,4.3,2.61,2.75,2.92,3.78,2.25,2.84,3.33,2.39)
Tsoil=c(30.01,27.72,34.91,21.96,22.83,29.65,20.56,23.72,33.96,31.57,19.15,28.49,30.02,20.14,30.23,33.99,32.58,17.87,19.78)
Pw=c(30,29,24,22,22,41.2,-33.1,-49.21,29.6,24.5,-6.1544,42.8,63,55.5,43.3,38.5,87.2,-0.32,79.5)
Rui's code seems to elegantly do what I was attempting (and will try the
Boolian version next), but I'm having initial parameterization problems.
I've tried doing what I've done before, which is use the excel solver to
give me some simple early estimates of params, but it doesn't appear to be
helping in this case.
nlstest=nls(Cfl~K*ifelse(Pw> z, 1, exp(-a*Tsoil))*exp(-b*Pw)+c,
start=c(K=0.14, a=0.01, b=0.04, c=3.5, z=5), trace=T)
I'll keep chugging away at this. Thank you for your help!
If the above data is all you have, I wouldn't chugg too long.
Looking at plots of Cfl~Pw and Cfl~Tsoil says to me that you
have far too much variability to fit your model.
You might find this plot instructive:
library(rgl)
plot3d(Pw, Tsoil, Cfl, col=1+(Pw<5), size=5)
Peter Ehlers
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and provide commented, minimal, self-contained, reproducible code.
