[R] Multivariate linear regression
Berton Gunter
gunter.berton at gene.com
Thu Apr 6 01:31:39 CEST 2006
Ummm...
If y is unrelated to x, then why would one expect any reasonable method to
show a greater or lesser relationship than any other? It's all random. Of
course, put enough random regressors into/"tune" the parameters enough of
any regression methodology and you'll be able to precisely predict the data
at hand -- but **only** the data at hand. I should note that such work
apparently frequently appears in various sorts of "informatics"/"data
mining"/"omics"/etc. journals these days, as various papers demonstrating
the irreproducibility of numerous purported discoveries have infamously
demonstrated. Let us not forget Occam!
Just being cranky ...
-- Bert Gunter
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Nagu
> Sent: Wednesday, April 05, 2006 3:52 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Multivariate linear regression
>
> Hi,
>
> I am working on a multivariate linear regression of the form y = Ax.
>
> I am seeing a great dispersion of y w.r.t x. For example, the
> correlations between y and x are very small, even after using some
> typical transformations like log, power.
>
> I tried with simple linear regression, robust regression and ace and
> avas package in R (or splus). I didn't see an improvement in the fit
> and predictions over simple linear regression. (I also tried this with
> transformed variables)
>
> I am sure that some of you came across such data. How did you
> deal with it?
>
> Linear regressions are good for the data like y = x +
> 0.01Normal(mu,sigma2) i.e. a small noise (data observed in a lab). But
> linear regressions are bad for large noise, like typical market (or
> survey) data.
>
> Thank you,
> Nagu
>
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