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
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
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