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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