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:r...@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.ruckst...@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.ruckst...@zhaw.ch
WWW : http://www.idp.zhaw.ch
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