> On 14 Nov 2015, at 16:15, Lorenzo Isella <lorenzo.ise...@gmail.com> wrote:
>
> Dear All,
> I am using optim() for a relatively simple task: a linear model where
> instead of minimizing the sum of the squared errors, I minimize the sum
> of the squared relative errors.
> However, I notice that the default algorithm is very sensitive to the
> choice of the initial fit parameters, whereas I get much more stable
> (and therefore better?) results with the BFGS algorithm.
> I would like to have some feedback on this (perhaps I made a mistake
> somewhere).
> I provide a small self-contained example.
> You can download a tiny data set from the link
>
> https://www.dropbox.com/s/tmbj3os4ev3d4y8/data-instability.csv?dl=0
>
> whereas I paste the script I am using at the end of the email.
> Any feedback is really appreciated.
> Many thanks
>
The initial parameter values for the percentage error variant are not very good.
If you print min.perc_error(data,par_ini2) you can see that.
Try
par_ini2 <- c(1e-4,1e-4,1e-4)
and you'll get results that are closer to each other.
The rest is up to you.
Berend
> Lorenzo
>
> ################################################################
>
> min.perc_error <- function(data, par) {
> with(data, sum(((par[1]*x1 + par[2]*x2+par[3]*x3 -
> y)/y)^2))
> }
>
> par_ini1 <- c(.3,.1, 1e-3)
>
> par_ini2 <- c(1,1, 1)
>
>
> data <- read.csv("data-instability.csv")
>
> mm_def1 <-optim(par = par_ini1
> , min.perc_error, data = data)
>
> mm_bfgs1 <-optim(par = par_ini1
> , min.perc_error, data = data, method="BFGS")
>
> print("fit parameters with the default algorithms and the first seed
> ")
> print(mm_def1$par)
>
> print("fit parameters with the BFGS algorithms and the first seed ")
> print(mm_bfgs1$par)
>
>
>
> mm_def2 <-optim(par = par_ini2
> , min.perc_error, data = data)
>
> mm_bfgs2 <-optim(par = par_ini2
> , min.perc_error, data = data, method="BFGS")
>
>
>
>
> print("fit parameters with the default algorithms and the second seed
> ")
> print(mm_def2$par)
>
> print("fit parameters with the BFGS algorithms and the second seed ")
> print(mm_bfgs2$par)
>
> ______________________________________________
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