From Torsten.Hothorn@rzmail.uni-erlangen.de  Wed Apr 18 10:44:11 2001
From: Torsten.Hothorn@rzmail.uni-erlangen.de (Torsten Hothorn)
Date: Wed, 18 Apr 2001 11:44:11 +0200 (MEST)
Subject: [Rd] PR#896
In-Reply-To: <x24rvo7ex8.fsf@blueberry.kubism.ku.dk>
Message-ID: <Pine.LNX.4.21.0104181142220.11968-100000@artemis.imbe.med.uni-erlangen.de>

On 16 Apr 2001, Peter Dalgaard BSA wrote:

> Torsten Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de> writes:
> 
> > > c(3.770684,4.654342,3.496403,1.772743,1.624953,2.645835,3.0994
> > > 77,1.706758,3.507709,1.982924)
> > > y<-c(-0.8161288,0.1632923,0.6421997,1.9270846,-
> > > 0.4668112,0.3587806,0.3312529,-0.5393900,
> > > 0.1057892,1.7963575)
> > 
> > `wdiff' is used for the computation of asymptotic confidence intervals for
> > samples with m,n > 50. I simulated the type I error rate and it works for
> > such large sample sizes. So maybe the problems are due to n.x = n.y = 10 ?
> > 
> > Torsten
> 
> I think the real problem is here:
> 
> > > ol<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.05))$minimum
> > > om<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.5))$minimum
> > > ou<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.95))$minimum
> > > 
> > > abline(v=ol)
> > > abline(v=om,lty=4)
> > > abline(v=ou,lty=7)
> 
> Optimize() is a gradient algorithm and wdiff is not smooth, so the
> algorithm terminates at a stationary non-optimal point. Using optim()
> with the default Nelder-Mead instead seemed to work better, although
> not perfectly. (I played with this before going on Easter holiday, and
> I'm not going to reconstruct exactly what I did then just now....)

Yes. We replaced `optimize' by `uniroot' in `wilcox.test' and use `optim'
in `ansari.test' now. 

Torsten

> 
> -- 
>    O__  ---- Peter Dalgaard             Blegdamsvej 3  
>   c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
>  (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
> ~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk)             FAX: (+45) 35327907
> 

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[Rd] PR#896
Torsten Hothorn