# Re: [R] NLS plinear question

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Tue, 06 May 2008 22:22:10 +0100 (BST)

0^(-0.2) = Inf, so you started with an infinite prediction for your first point and hence an infinite sum of squares.

On Tue, 6 May 2008, Rick DeShon wrote:

> Hi All.
>
> I've run into a problem with the plinear algorithm in nls that is confusing
> me.
>
> Assume the following reaction time data over 15 trials for a single unit.
> Trials are coded from 0-14 so that the intercept represents reaction time in
> the first trial.
>
> trl RT
> 0 1132.0
> 1 630.5
> 2 1371.5
> 3 704.0
> 4 488.5
> 5 575.5
> 6 613.0
> 7 824.5
> 8 509.0
> 9 791.0
> 10 492.5
> 11 515.5
> 12 467.0
> 13 556.5
> 14 456.0
>
> Now fit a power function to this data using nls with the plinear algorithm
>> fit.pw <-nls(RT ~ cbind(1,trl, trl^p), start = c(p = -.2), algorithm =
> "plinear", data=df.one)
>
> Yields the following error message....
> "Error in numericDeriv(form[[3]], names(ind), env) :
> Missing value or an infinity produced when evaluating the model"
>
> Now, recode trial from 1-15 and run the same model.
>> fit.pw <-nls(RT ~ cbind(1,trl, trl^p), start = c(p = -.2), algorithm =
> "plinear", data=df.one)
>
> Seems to work fine now...
> Nonlinear regression model
> model: RT ~ cbind(1, trl, trl^p)
> data: df.one
> p .lin1 .lin.trl .lin3
> -0.2845 200.3230 -8.9467 904.7582
> residual sum-of-squares: 555915
>
> Number of iterations to convergence: 11
>
> Any idea why having a zero for the first value of X causes this problem?
>
> Thanks in advance,
>
> Rick DeShon
>
> [[alternative HTML version deleted]]
>
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>

```--
Brian D. Ripley,                  ripley_at_stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Received on Tue 06 May 2008 - 21:41:51 GMT

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