[R] nls model fitting errors

[Peter Ehlers]

On 2010-06-10 8:27, Graves, Gregory wrote:
> What am I failing to understand here?
>

Several things; see below.

>
>
> The script below works fine if the dataset being used is
>
> DNase1<- DNase[ DNase$Run == 1, ]  per the example given in
> help(nlrob).
>
> Obviously, I am trying to understand how to use nls and nlrob to fit
> curves to data using R.
>
>
>
> #package=DAAG
>
> attach(codling)
>
> plot(pobs~dose)
>
> #next command returns 'step factor reduced below min factor error'
>
> m.nls<- nls( pobs ~ a/(1 + exp(( b - log(dose) )/c ) ),
>
>                    data = codling,
>
>                    start = list( a = 3, b = 0, c = 1 ),
>
>                    trace = TRUE )
>
> s<-seq(min(pobs), max(pobs), .01)

If pobs is your y-value (response) then what is 's' for?
>
> p.nls<-predict(m.nls,list(pobs=s))

Ah, you want predicted *response* values; so feed
appropriate *predictor* values to predict().
But I don't see how this would not give you an error
anyway if your nls() call resulted in an error.

>
> lines(s,p.nls,col='blue')  #generates 'x and y lengths differ' error

This error would result from incorrectly generating p.nls,
(but I don't see how you got R to give you that anyway).

I have 4 suggestions:
1. Define a new variable ldose=log(dose);
    It's usually less confusing to work on
    the log-scale in these cases.
    Then plot pobs vs ldose.

2. From the plot you should notice that the
    left asymptote is not likely to be zero
    (which is what your model assumes). It's
    roughly 0.2.

3. The right asymptote should probably be 1.0
    since pobs is a proportion. So a start value
    of a=3 in your model makes no sense as you
    would also quickly see if you plotted the
    curve represented by your model on the
    plot of the data (use: curve(...,add=TRUE)).

4. Try this model:

    pobs ~ (A + exp((ldose - B)/C)) / (1 + exp((ldose - B)/C))

    with starting values: A = 0.2, B = 3, C = 1


  -Peter Ehlers

>
>
> Gregory A. Graves, Lead Scientist
>
> Everglades REstoration COoordination and VERification (RECOVER)
>
> Restoration Sciences Department
>
> South Florida Water Management District
>
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>
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