Thanks for your help, what I meant was that each observation x had a
corresponding count to them, and I wanted to use these counts as weights in
the optim (so that the optim process would give more weight to the
measurements that had more counts).
I had forgotten if the weights should accounted for in the ypred formula, or
in the sum of squares as you mentioned.
On Fri, Sep 23, 2011 at 3:10 PM, Rolf Turner <rolf.tur...@xtra.co.nz> wrote:
>
>
> 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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