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
recall that 0 ^{-.2} = 1/0^{.2}, and that dividing by 0 gives Inf.
so when 0 is in trl, part of your model for RT is Inf:
> trl <- 0:14
> p <- -.2
> cbind(1,trl, trl^p)
trl
[1,] 1 0 Inf
[2,] 1 1 1.000
[3,] 1 2 0.8705506
[4,] 1 3 0.8027416
[5,] 1 4 0.7578583
[6,] 1
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
01132.0
1 630.5
2
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