# [R] (sans objet)

Prof Brian Ripley ripley at stats.ox.ac.uk
Tue Jun 15 14:50:16 CEST 2004

```On Tue, 15 Jun 2004, Benjamin Esterni wrote:

> I have a problem with the dr function: "dimension reduction".

It seems that you are using it inappropriately.

>
> #let be X a matrix 50*100:
>
> library(dr);

You should not be terminating lines with ;.  This is R, not C.

> X<- matrix(rnorm(50*100,5,1),50,100);
>
> #and let be Y a vector response:
> Y<- sample(0:1,50,replace=T);
>
> #I choose (for the expérience, but in reality i don't have it) a few variables #which are censed to explain Y:
>
> index<- sample(1:100,10);
> X[Y==1,index]<-10*X[Y==1,index];
>
> #so now I want to proceed to a logistic regression, but I don't know the vector #"index". So I have to reduce the dimension of X, and that's why I use the function #"dr" (dr package).
>
> model<- dr(Y~X,family="binomial",method="phdy");
>
> edr<- dr.direction(model);
>
> #And now my problem: I hope that edr is a matrix constructed with linear
> #combinaison of X, prinipally the "index" vectors of X. But in reality
> it's not the #situation:

Your variables have no predictive power at all.  Look at

pairs(cbind(Y, edr))

> library(nnet);
> fit<-multinom(Y~.,data=data.frame(edr));

Take a look at this.  The fit is useless, because your variables are.

> pred<-predict(fit,data.frame(edr));
> table(Y,pred)
> 0  21
> 0  19

Given that Y has 50 values and that table labels dims, how on earth did
you get that?

--
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

```