Dear All,
please help with writing the function for the following:
we have data frame "raw"
D1 <-c(2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20,
50, 50, 50, 50, 50)
D2 <-c(0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2, 5, 0.2, 0.5, 1, 2,
5, 0.2, 0.5, 1, 2, 5)
E <-c(76.3, 48.8, 44.5, 15.5, 3.21, 56.7, 47.5, 26.8, 16.9, 3.25, 46.7, 35.6,
21.5, 11.1, 2.94, 24.8, 21.6, 17.3, 7.78, 1.84, 13.6, 11.1, 6.43, 3.34, 0.89)
raw <-data.frame(D1,D2,E)
reasonable starting parameters for nls (to the best of my knowledge):
start <-c(p1=8,p2=80,p3=-0.7,p4=-2.5,p5=0.3,p6=0.7)
I would like to fit this model:
1 =
D1/(p1*((E/(p2-E))^(1/p3)))+D2/(p6*((E/(p2-E))^(1/p4)))+(p5*D1*D2)/(p1*p6*((E/(p2-E))^(0.5/p3+0.5/p4)))
to the data in "raw" using nls(). The weighting of the fit in this case should
be done using the inverse of the variance of raw$E. Having the model equal to 1
makes it quiet difficult for me to see or understand how this can be done
(versus the "usual" nls(y~p1*x+p2,...)) using the software.
As always, your help is greatly appreciated.
Sincerely,
Andras
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