Thank you for very informative answers. This is great help. Peter, thanks
for correcting the model. That was exactly what I meant - apologies for the
typo.
I was able to run the analysis on some simulated data and the subset of data
that I had posted earlier. However, when I apply the analysis to full
dataset, I usually get the following errors-
fm2 <- nls(y ~ b + a * (exp(m1 * -x) + exp(m2 * -x)), start = list(a = 1, b
= 0, m1 = 0.1, m2 = 0.5), trace = TRUE)
84.42045 : 1.0 0.0 0.1 0.5
0.1593364 : 0.072399953 -0.000226868 0.101135423 0.571436093
0.09032837 : 7.897930e-02 7.905359e-06 1.271435e-01 1.872206e+00
0.06834495 : 0.0808842841 0.0005815865 0.1725302473 4.9048478439
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
OR
Error in nls(y ~ b + a * (exp(m1 * -x) + exp(m2 * -x)), start = list(a = 1,
:
number of iterations exceeded maximum of 50
Is there a way to optimize the start values? I was unable to try the RStudio
solution as RStudio keeps crashing on my computer.
Also is there any way to set the start value in NLS - I mean if I want to
fit the model to only a subset of data starting at x = 10, how can I specify
that?
Thanks,
Diviya
On Sun, Jun 12, 2011 at 7:19 PM, Dennis Murphy <djmu...@gmail.com> wrote:
> Hi:
>
> If you use RStudio, then you can use its manipulate package to figure
> out starting values for the model visually through sliders. Walmes
> Zeviani posted the template of how to do this last week; I've just
> adapted it for the models under consideration. It's interesting to
> play with this because it shows how strongly some of the parameters
> are correlated with one another...and it's fun :) Thanks to Walmes for
> the excellent tutorial example.
>
> Firstly, x and y (or the inputs in general) need to be in the global
> environment as vectors of the same length.
> [RStudio is a GUI, so I'm assuming that you run things from its
> command line.] Load the manipulate package (which comes with RStudio)
> and use the manipulate() function to create a plot of the data and fit
> a curve to it. The sliders adjust the parameter values and you play
> with them until the curve fits closely to the data. The range of the
> sliders should match the range of plausible values you think they
> might take. The output of the manipulate() function is a vector of
> starting values that you can pass into nls(), which is done by closing
> the window containing the sliders.
>
> # Model 1:
>
> x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
> y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436,
> -0.000006, -0.004626, -0.004626, -0.004626, -0.004626)
> plot(x, y) # Initial plot of the data
> start <- list() # Initialize an empty list for the starting values
>
> library(manipulate)
> manipulate(
> {
> plot(x, y)
> k <- kk; b0 <- b00; b1 <- b10
> curve(k*exp(-b1*x) + b0, add=TRUE)
> start <<- list(k=k, b0=b0, b1=b1)
> },
> kk=slider(0.01, 0.2, step = 0.01, initial = 0.1),
> b10=slider(0.01, 0.4, step = 0.01, initial = 0.3),
> b00=slider(-0.01, -0.001, step=0.001,initial= -0.005))
>
> # When done, start() is a list of named parameters in
> # the global environment
> # Model fit:
> fit1 <- nls(y ~ k*exp(-b1*x) + b0, start = start)
> summary(fit1)
>
> ### Model 2: [following Peter Dalgaard's suggested model]
> ### Use the estimates from fit1 and shrink b1 to anticipate
> ### potential effect of b2; the initial estimate in the slider for
> ### b1 will be too big, so it needs to be shrunk (a lot :)
> start <- list()
> manipulate(
> {
> plot(x, y)
> k <- kk; b0 <- b00; b1 <- b10; b2 <- b20
> curve(k*(exp(-b1*x) + exp(-b2*x)) + b0, add=TRUE)
> start <<- list(k=k, b0=b0, b1=b1, b2 = b2)
> },
> kk=slider(0.01, 0.1, step = 0.01, initial = 0.04),
> b10=slider(0.01, 0.4, step = 0.01, initial = 0.3),
> b20 = slider(0.01, 0.1, step = 0.005, initial = 0.05),
> b00=slider(-0.01, -0.001, step=0.001,initial= -0.004))
>
> fit2 <- nls(y ~ k*(exp(-b1*x) + exp(-b2*x)) + b0, start = start)
> summary(fit2)
>
> anova(fit1, fit2)
>
>
> HTH,
> Dennis
>
> On Sun, Jun 12, 2011 at 9:57 AM, Diviya Smith <diviya.sm...@gmail.com>
> wrote:
> > Hello there,
> >
> > I am trying to fit an exponential fit using Least squares to some data.
> >
> > #data
> > x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
> > y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436,
> > -0.000006, -0.004626, -0.004626, -0.004626, -0.004626)
> >
> > sub <- data.frame(x,y)
> >
> > #If model is y = a*exp(-x) + b then
> > fit <- nls(y ~ a*exp(-x) + b, data = sub, start = list(a = 0, b = 0),
> trace
> > = TRUE)
> >
> > This works well. However, if I want to fit the model : y = a*exp(-mx)+c
> then
> > I try -
> > fit <- nls(y ~ a*exp(-m*x) + b, data = sub, start = list(a = 0, b = 0, m=
> > 0), trace = TRUE)
> >
> > It fails and I get the following error -
> > Error in nlsModel(formula, mf, start, wts) :
> > singular gradient matrix at initial parameter estimates
> >
> > Any suggestions how I can fix this? Also next I want to try to fit a sum
> of
> > 2 exponentials to this data. So the new model would be y = a*exp[(-m1+
> > m2)*x]+c . Any suggestion how I can do this... Any help would be most
> > appreciated. Thanks in advance.
> >
> > Diviya
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@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.
> >
>
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