Sunday, March 02, 2003, 9:48:26 PM, you wrote:
MK> [EMAIL PROTECTED] wrote:
>> You are not solving the same problem, which is more than a little `odd'.
MK> Why?
MK> >>> want to choose the three parameters so that
MK> these three integrals are as close to, resp., 2300, 4600 and 5800 as
MK> possible. As I have three equations with three unknowns, I expect the
MK> exact fit, i.e., the SS (see below) to be zero.
Good evening to all!
In fact, I want to estimate three unknown parameters TETA[1],TETA[2]
(the scale parameter) and TETA[3]. What I know is 10 numbers
aa <- c(2300,4600,5800,
7100,...,37700) and these are the empirical counterparts of
the integrals over [0,0.1],...[0.9,1]. I don't know whether this is
correct but in order to find the parameters I minimize the
sum((integr-aa)^2) (kind of the "moment method"). As I had problems
with optim, I reduced the problem to three intervals.
I have to admit, I'm still fighting with optim. Following Prof
Ripley's suggestion, I replaced
res <- optim(init,LSS,aa=aa,method = "L-BFGS-B",lower=c(0,0,0.5))
by
res <- optim(init,LSS,aa=aa,method = "L-BFGS-B",lower=c(0,0,0.5),
control=list(parscale=c(1,1000,1)))
Now I have
> source("C:/Program Files/R/integral.R")
[1] 2.50 7000.00 0.84 # initial
[1] 2.052587 66734.476822 42.110597 # final
[1] 42820.43
instead of what I had earlier
>[1] 2.50 7000.00 0.84 # initial
>[1] 2.3487221 6999.9999823 0.5623628 # final
>[1] 75613.05
Now SS=42820, but it is still far from zero. I agree, I did not
implemented all what I was advised to, but it could take some time.
Many thanks for the help.
Remigijus
***************************************************
***************************************************
>Do read the help page. It says:
>
> [...]
>
>`parscale' A vector of scaling values for the parameters.
> Optimization is performed on `par/parscale' and these should
> be comparable in the sense that a unit change in any element
> produces about a unit change in the scaled value.
******************************************************
******************************************************
>>Dear all,
>>
>>I have a function MYFUN which depends on 3 positive parameters TETA[1],
>>TETA[2], and TETA[3]; x belongs to [0,1].
>>I integrate the function over [0,0.1], [0.1,0.2] and
>>[0.2,0.3] and want to choose the three parameters so that
>>these three integrals are as close to, resp., 2300, 4600 and 5800 as
>>possible. As I have three equations with three unknowns, I expect the
>>exact fit, i.e., the SS (see below) to be zero. However, the optim
>>function never gives me what I expect, the minimal SS value(=res$value)
>>never comes close to zero, the estimates of the parameters, res$par,
>>wildly depends on init etc.
>>I would be grateful for any comments on this miserable situation.
>>
>>aa <- c(2300,4600,5800)
>>init <- c(2.5,8000,0.84) # initial values of parameters
>>print(init)
>>###################
>>myfun <- function(x,TETA) TETA[2]*(((1-x)^(-1/TETA[3]))-
>>1)^(1/TETA[1])
>>###################
>>x <- seq(0,0.3,by=0.01)
>>plot(x,myfun(x,init),type="l")
>>###################
>>LSS <- function(teta,aa)
>>{
>>integr <- numeric(3)
>> for(i in 1:3)
>> {integr[i] <- 10*integrate(myfun,
>> lower=(i-1)/10,upper=i/10,TETA=teta)$value
>> }
>>SS <- # SS=Sum of Squares
>>SS
>>}
>>####################
>>res <- optim(init,LSS,aa=aa,
>>method = "L-BFGS-B",lower=c(0,0,0.5))
>>print(res$par)
>>print(res$value)
>>
>>
>>
>>>source("C:/Program Files/R/integral.R")
>>
>>[1] 2.50 7000.00 0.84 # initial
>>[1] 2.3487221 6999.9999823 0.5623628 # final
>>[1] 75613.05 # minSS
>>
>>>source("C:/Program Files/R/integral.R")
>>
>>[1] 2.5 15000 0.84 # initial
>>[1] 2.125804 14999.999747 2.241179 # final
>>[1] 50066.35 # minSS
>>
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