[RsR] robustbase: precision of nlrob with plinear algorithm.

[Andreas Ruckstuhl]

Maybe.- Maybe not, because the result of the plinear estimates is very 
strange and might be too far off the final estimates.
At the very moment, I have absolutely no trust in the plinear estimate 
and I prefer to disregard themuntil I understand better what is going on.

Andreas

Am 01.02.2016 um 21:34 schrieb Jerry Lewis:
> Thanks for your response.  Would a better workaround be to use the plinear estimates as starting values for the full Gauss-Newton?
>
> Jerry
>
> -----Original Message-----
> From: Andreas Ruckstuhl [mailto:rkst using zhaw.ch]
> Sent: Monday, February 01, 2016 3:20 PM
> To: Jerry Lewis
> Cc: Martin Maechler; r-sig-robust
> Subject: Re: robustbase: precision of nlrob with plinear algorithm.
>
> Dear Jerry
>
> As in the help file to nlrob is noted the conditionallinearity approach fails for robust fitting methods. Unfortunately not anymore by an error message.
>
> If you checkthe output more carefully, you will note that the result of nlrob(... , algorithm="plinear" ) is not reliable:
> DNrfit1 <- nlrob( density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
>                    data=DNase1, start=list(Asym=3,xmid=0,scal=1) )
> DNrfit2 <- nlrob( density ~   1 /(1 + exp(( xmid - log(conc) )/scal ) ),
>                    data=DNase1, start=list(xmid=0,scal=1), algorithm="plinear" )
>
> summary(DNrfit1)
> ## Residuals:
> ##        Min         1Q     Median         3Q Max
> ## -0.0322811 -0.0130976 -0.0008932  0.0095784 0.0404174
>
> summary(DNrfit2)
> ## Residuals:
> ##      Min       1Q   Median       3Q      Max
> ## 0.003927 0.107372 0.280145 0.641955 1.002490
>
> Here all residuals are positive!
>
> I have not (jet) understood what goes wrong. Hence,we should disable this argument (for the moment).
>
> Thanks for your hint.
>
> All the best
> Andreas
>
>
>
> Am 28.01.2016 um 06:37 schrieb Jerry Lewis:
>> In nls, plinear simplifies the search for the LS estimate by
>> simplifying the optimization through dimension reduction.  Output
>> (including standard errors are completely equivalent, provided that
>> that the higher dimensional ordinary Gauss-Newton optimization is able
>> to adequately find the least squares estimates.
>>
>> The first example for nlrob suggests that this is only partially true
>> for nlrob.  To the displayed precision almost everything (robustness
>> weights, robust residual SE and parameter estimates) are identical
>> between plinear and ordinary Gauss-Newton, but the standard errors of
>> parameter estimates from plinear are 2.769 times larger for each
>> parameter.  Is this a bug in the code, or is there a theoretical
>> reason why this should be so?
>>
>> Thanks,
>>
>> Jerry W. Lewis, PhD
>>
>> Principal Biostatistician
>>
>> Biogen
>>
>> 225 Binney St
>>
>> Cambridge, MA 02142
>>
>> library(robustbase)
>>
>> DNase1 <- DNase[ DNase$Run == 1, ]
>>
>> with(DNase1,plot(conc,density,log="x"))
>>
>> # plinear and Gauss-Newton get the same robustness weights, robust
>> residual SE, and parameter estimates,
>>
>> # why then are the standard errors different?
>>
>> # Given that, why are the plinear standard errors larger when when the
>> comment in the example
>>
>> summary( nlrob( density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
>>
>>           data=DNase1, start=list(Asym=3,xmid=0,scal=1) ) )
>>
>> summary( nlrob( density ~   1 /(1 + exp(( xmid - log(conc) )/scal ) ),
>>
>>           data=DNase1, start=list(xmid=0,scal=1), algorithm="plinear" )
>> )
>>
> --
> ----------------------------------------------------------------------
>
> Prof. Dr. Andreas Ruckstuhl
> Schwerpunktleiter Statistische Datenanalyse IDP Institut für Datenanalyse und Prozessdesign ZHAW Zürcher Hochschule für Angewandte Wissenschaften
> Rosenstrasse 3                    Tel.  : +41 (0)58 934 78 12
> Postfach                          Fax   : +41 (0)58 935 78 12
> CH-8401 Winterthur                e-Mail:Andreas.Ruckstuhl using zhaw.ch
>                                     WWW   :http://www.idp.zhaw.ch
> ----------------------------------------------------------------------
>

-- 
----------------------------------------------------------------------

Prof. Dr. Andreas Ruckstuhl
Schwerpunktleiter Statistische Datenanalyse
IDP Institut für Datenanalyse und Prozessdesign
ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Rosenstrasse 3                    Tel.  : +41 (0)58 934 78 12
Postfach                          Fax   : +41 (0)58 935 78 12
CH-8401 Winterthur                e-Mail: Andreas.Ruckstuhl using zhaw.ch
                                   WWW   : http://www.idp.zhaw.ch