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> > > 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 >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 -- View this message in context: http://r.789695.n4.nabble.com/nls-help-tp4123876p4124164.html Sent from the R help mailing list archive at Nabble.com.
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:> 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 > > > -- > View this message in context: http://r.789695.n4.nabble.com/nls-help-tp4123876p4123876.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >