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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