)
}
(Note that your ``res - ...'' and ``return(res)'' are unnecessary.)
optim(c(0.0,1.0),logis.op,x=**d1_all$SOA,y=as.numeric(md1[,**i]),
w=whatever weights you had in
mind)
HTH
cheers,
Rolf Turner
On 23/09/11 13:47, Ahnate Lim wrote:
I
I realize this may be more of a math question. I have the following optim:
optim(c(0.0,1.0),logis.op,x=d1_all$SOA,y=as.numeric(md1[,i]))
which uses the following function:
logis.op - function(p,x,y) {
ypred - 1.0 / (1.0 + exp((p[1] - x) / p[2]));
res - sum((y-ypred)^2)
return(res)
}
this
predict?
On Wed, Jan 26, 2011 at 11:41 PM, Gavin Simpson gavin.simp...@ucl.ac.ukwrote:
On Wed, 2011-01-26 at 19:25 -1000, Ahnate Lim wrote:
Even when I try to predict y values from x, let's say I want to predict y
at
x=0. Looking at the graph from the provided syntax, I would expect y
Dear R-help,
I have fitted a glm logistic function to dichotomous forced choices
responses varying according to time interval between two stimulus. x values
are time separation in miliseconds, and the y values are proportion
responses for one of the stimulus. Now I am trying to extrapolate x
...@comcast.netwrote:
On Jan 26, 2011, at 10:52 PM, Ahnate Lim wrote:
Dear R-help,
I have fitted a glm logistic function to dichotomous forced choices
responses varying according to time interval between two stimulus. x
values
are time separation in miliseconds, and the y values are proportion
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