Hi,
I am rather new to R, but both myself and another much more experience user cannot
figure this out. I have a collegue who has a 800+ nonlinear regressions to run for
seed germination (different species, treatments, etc.) over time. I created a looping
structure to extract the parameters from each regression; I will then use the
parameters themselves for further analysis. I would like to fit a 4-parameter,
sigmoidal shape Weibull because it it paramatized to have a maximum germination rate
(max), a time lag for the start of germination (lag), a germination rate (rate) and a
shape parameter (shape).
Here is a copy of this bit of the code:
start.est=list(max=max(df$Y),rate=1/(df$X[tx]-((df$X[tz-1]+df$X[tz])/2)),
lag=(df$X[tz-1]+df$X[tz])/2,shape=1.1)
nls.control(maxiter=1000,minFactor=1/8192)
fit<-nls(Y ~ max*(1 - exp(-(rate*(X-lag))^shape)),df,start=start.est)
Here is one column of the data (X=Julian day, Y = % germination):
X Y
1 111 0.0000000
2 125 0.0000000
3 131 0.0000000
4 138 0.3076923
5 145 0.4260355
6 152 0.4733728
7 159 0.5443787
8 166 0.5680473
9 173 0.5680473
10 180 0.5917160
11 187 0.5917160
12 194 0.5917160
13 201 0.6153846
14 208 0.6153846
15 215 0.6153846
16 223 0.6153846
17 229 0.6153846
18 236 0.6153846
19 245 0.6153846
Here is the error I keep getting:
Error in numericDeriv(form[[3]], names(ind), env) :
Missing value or an Infinity produced when evaluating the model
I have tried these things to get this to work:
1) Mess with the starting values quite a bit
2) Seed the regression with linear estimates between my known points (i.e., figure
out daily averages). I then wanted to regress on these to get starting
estimates to use with the real data.
3) Drop out all the 0s in the seeded data
4) Add jitter to the seeded data
I should note that this worked to get a 3-parameter Gompertz to work, but that
functional form is more difficult to interpret biologically.
I am out of ideas. Thoughts anyone? I would appreciate any help.
Mike Saunders
Research Assistant
Department of Forest Ecosystem Sciences
University of Maine
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