[R] Optim() and Instability

[Gabor Grothendieck]

I meant the parscale parameter.

On Sat, Nov 14, 2015 at 10:30 AM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
> Tyipcally the parameters being optimized should be the same order of
> magnitude or else you can expect numerical problems.  That is what the
> fnscale control parameter is for.
>
> On Sat, Nov 14, 2015 at 10:15 AM, Lorenzo Isella
> <lorenzo.isella at 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
>>
>> 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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>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com



-- 
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com