I'm not at all sure that I understand your question, but since (as far
as I am aware) no-one else has answered, I'll give it a go.
The puzzle, to me, is what you mean by ``I would like to add weights
to optim.'' What do you mean ``add weights''?
If you want to minimize a weighted sum of squares, it seems to me to
be trivial:
logis.op <- function(p,x,y,w=1) {
ypred <- 1.0 / (1.0 + exp((p[1] - x) / p[2]));
sum(w*(y-ypred)^2)
}
(Note that your ``res <- ...'' and ``return(res)'' are unnecessary.)
optim(c(0.0,1.0),logis.op,x=d1_all$SOA,y=as.numeric(md1[,i]),
w=<whatever weights you had in
mind>)
HTH
cheers,
Rolf Turner
On 23/09/11 13:47, Ahnate Lim wrote:
I realize this may be more of a math question. I have the following optim:
optim(c(0.0,1.0),logis.op,x=d1_all$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)
}
I would like to add weights to the optim. Do I have to alter the logis.op
function by adding an additional weights parameter? And if so, how would I
change the ypred formula? Would I just substitute x with x*w
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