R help - Sep 2005 - Predicting responses using ace

Hello everybody,

I'm a new user of R and I'm working right now with the ACE function
from the acepack library. I Have a question: Is there a way to predict
new responses using ACE? What I mean is doing something similar to the
following code that uses PPR (Projection Pursuit Regression):

library(MASS)
x <- runif(20, 0, 1)
xnew <- runif(2000, 0, 1)
y <- sin(x)
a <- ppr(x, y, 2)
ynew <- predict(ppr, xnew)

Any help would be much appretiated, Thanks in advance,
Luis Pineda

Luis Pineda wrote:
> Hello everybody,
> 
> I'm a new user of R and I'm working right now with the ACE function
> from the acepack library. I Have a question: Is there a way to predict
> new responses using ACE? What I mean is doing something similar to the
> following code that uses PPR (Projection Pursuit Regression):
> 
> library(MASS)
> x <- runif(20, 0, 1)
> xnew <- runif(2000, 0, 1)
> y <- sin(x)
> a <- ppr(x, y, 2)
> ynew <- predict(ppr, xnew)
> 
> Any help would be much appretiated, Thanks in advance,
> Luis Pineda
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
> 
Look at the areg.boot function in the Hmisc package, and its associated 
predict method.

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

I sent this email before, but I got a r-help-bounce message and I don know 
if it got to the m-list. Sorry if you had already seen it.

I'm using the areg.boot function to do an ace regression. So far I've
been
able to do some simple running tests to fit a model with some input data, 
predict the response with new data, and find the inverse transform of such 
prediction (apparently). The commands I'm using are the following:

library(MASS)
library(Hmisc) 
xyt = read.table("tra100unifxy.dat") #2x100 table of training data
(expl.
variables)
zt = read.table("tra100unifzR.dat") #1x100 table of training data
(resp.
variables)
zs = read.table("sim10000z.dat") #2x10000 table of new data to predict
(expl. variables)
xys = read.table("sim10000xy.dat") #1x10000 table of expected
responses
x = xyt[,1]
y = xyt[,2]
z = zt[,1]
xynew = data.frame(x=xys[,1],y=xys[,2])
ace.r = areg.boot(z ~ x + y, B = 100)
f = Function(ace.r, ytype='inverse')
za = f$z(predict(ace.r,xynew))

I have a couple question:

1.) Is that the correct way of finding the inverse transform for the 
responses?
2.) I'm evaluating the model's goodness of fit using the Fraction of 
Variance Unexplained, which I'm calculating as:

rsa = za - zs
FVUa = sum(rsa*rsa)/(10000*var(zs)) #10000 is the size of the test set

The thing is I'm not getting satisfactory results. Is there a way to improve
the results of the regression?. At the moment I'm not too confident with the
formula I'm using as a parameter for areg.boot, since the response variables
were generated as a substantially more complex function than z = x+y. I 
don't get this formula thing yet and maybe I'm passing a totally
unrelated
formula to the function.

On 9/7/05, Frank E Harrell Jr <f.harrell@vanderbilt.edu>
wrote:
> 
> Luis Pineda wrote:
> > Well, I had no idea, since I read this in the documentation:
> >
> > "|x| - for |transace| a numeric matrix. For |areg.boot| |x| may
be a
> > numeric matrix or a formula...||"
> >
> > Luis Pineda
> 
> Sorry about that - you are right.
> 
> Thomas - please debug the code to make areg.boot work with x = numeric
> matrix, or correct the help file. Thanks -Frank
> 
> --
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics Vanderbilt University
>
	[[alternative HTML version deleted]]