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nls help

4 messages · Dieter Menne, R. Michael Weylandt, chuck.01

#
Hello, 
I have data like the following:

datum <- structure(list(Y = c(415.5, 3847.83333325, 1942.833333325,
1215.22222233333, 
950.142857325, 2399.5833335, 804.75, 579.5, 841.708333325, 494.053571425
), X = c(1.081818182, 0.492727273, 0.756363636, 0.896363636, 
1.518181818, 0.499166667, 1.354545455, 1.61, 1.706363636, 1.063636364
)), .Names = c("Y", "X"), row.names = c(NA, -10L), class = "data.frame")


with(datum, plot(Y~X))

As you can see there is a non-linear association between X and Y, and I
would like to fit an appropriate model.  I was thinking an exponential decay
model might work well. 

I tried the following (a and k starting values are based off of a lm() fit),
but get an error. 

fit <- nls(Y ~ a*exp(-k * X), datum, start=c(a=3400, k=1867))

Error in nlsModel(formula, mf, start, wts) : 
  singular gradient matrix at initial parameter estimates

I have never attempted to fit a non-linear model before, and thus the model
may be inappropriately specified, or it is also possible that I have no idea
what I am doing. 

Would someone please offer some advice.

Thanks.
Chuck


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#
chuck.01 wrote
Try

plot(datum$X,datum$Y)

For more complex cases, plot the initial function you are trying to fit, but
in this case it is easy to see that k is more in the order of 2.

So try with k=2.

Dieter



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#
It's a scaling problem:

If you do this:

datum <- datum[order(datum$X),]

with(datum, plot(Y~X))
with(datum, lines(X, 3400*exp(-1867*X)))

you'll see that your initial guess is just so far gone that the nls()
optimizer can't handle it.

If you try a more reasonable initial guess it works fine:

fit <- nls(Y ~ a*exp(-k * X), datum, start=c(a=3400, k=1.867))

Michael
On Wed, Nov 30, 2011 at 12:14 PM, chuck.01 <CharlieTheBrown77 at gmail.com> wrote:
#
Ah, I see, thank you both. 




Michael Weylandt wrote
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