On 2010-07-07 19:19, Godfrey van der Linden wrote:
On 2010-07-03, at 17:43 , Peter Ehlers wrote:
On 2010-07-03 0:05, Godfrey van der Linden wrote:
G'day, All.
I have been trying to trackdown a problem in my R analysis script. I perform a
scale() operation on a matrix then do further work.
Is there any way of inverting the scale() such that
sX<- scale(X)
Xprime<- inv.scale(x); # does inv.scale exist?
resulting in Xprime_{ij} == X_{ij} where Xprime_{ij} \in R
There must be some way of doing it but I'm such a newb that I haven't been able
to find it.
Thanks
Godfrey
If your sX hasn't lost the "scaled:center" and
"scaled:scale" attributes that it got from the
scale() operation, then you can just reverse
the scaling procedure using those. Multiply
columns by the "scale" attribute, then add the
"center" attribute. Something like:
MN<- attr(sx, "scaled:center")
SD<- attr(sx, "scaled:scale")
Xprime<- t(apply(sx, 1, function(x){x * SD + MN}))
If the attributes have been lost by your further
work, then I'm afraid you're out of luck.
-Peter Ehlers
Thanks for this, I had forgotten the transpose function existed. I did maintain
the attributes, though I was surprised how many times I had to move them
manually in my script.
Anyway I also tried a functional programming solution and I'm sort of curious
what the differences are? I used rep to build out the SD and MN vectors.
Something like this (though I have lost the precise code now and would have to
regenerate it)
nr = nrow(sx)
Xprime = sx * rep(SD, each=nr) + rep(MN, each=nr);
Is there any way of determining which approach is more efficient?
Presumably by 'more efficient' you mean faster.
You can check with system.time() on a sufficiently
large matrix (1000-by-1000, say). Since the apply()
method loops over rows, it will be somewhat slower.
Also, your vector calculation will retain the
attributes of sx.
-Peter Ehlers
Thanks again.
Godfrey
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
