[R] scale or not to scale that is the question - prcomp
petr.pikal at precheza.cz
Wed Aug 19 16:14:24 CEST 2009
Duncan Murdoch <murdoch at stats.uwo.ca> napsal dne 19.08.2009 15:25:00:
> On 19/08/2009 9:02 AM, Petr PIKAL wrote:
> > Thank you
> > Duncan Murdoch <murdoch at stats.uwo.ca> napsal dne 19.08.2009 14:49:52:
> >> On 19/08/2009 8:31 AM, Petr PIKAL wrote:
> >>> Dear all
> > <snip>
> >> I would say the answer depends on the meaning of the variables. In
> >> unusual case that they are measured in dimensionless units, it might
> >> make sense not to scale. But if you are using arbitrary units of
> >> measurement, do you want your answer to depend on them? For example,
> >> you change from Kg to mg, the numbers will become much larger, the
> >> variable will contribute much more variance, and it will become a
> >> important part of the largest principal component. Is that sensible?
> > Basically variables are in percentages (all between 0 and 6%) except
> > which is present or not present (for the purpose of prcomp transformed
> > 0/1 by as.numeric:). The only variable which is not such is iep which
> > basically in range 5-8. So ranges of all variables are quite similar.
> > What surprises me is that biplot without scaling I can interpret by
> > variables while biplot with scaling is totally different and those two
> > pictures does not match at all. This is what surprised me as I would
> > expected just a small difference between results from those two
> > as all numbers are quite comparable and does not differ much.
> If you look at the standard deviations in the two cases, I think you can
> see why this happens:
> Standard deviations:
>  1.3335175 1.2311551 1.0583667 0.7258295 0.2429397
> Not Scaled:
> Standard deviations:
>  1.0030048 0.8400923 0.5679976 0.3845088 0.1531582
> The first two sds are close, so small changes to the data will affect
I see. But I would expect that changes to data made by scaling would not
change it in such a way that unscaled and scaled results are completely
> their direction a lot. Your biplots look at the 2nd and 3rd components.
Yes because grouping in 2nd and 3rd component biplot can be easily
explained by values of some variables (without scaling).
I must admit that I do not use prcomp much often and usually scaling can
give me "explainable" result, especially if I use it to "variable
reduction". Therefore I am reluctant to use it in this case.
when I try "more standard" way
> fit<-lm(iep~sio2+al2o3+p2o5+as.numeric(dus), data=rglp)
lm(formula = iep ~ sio2 + al2o3 + p2o5 + as.numeric(dus), data = rglp)
Min 1Q Median 3Q Max
-0.41751 -0.15568 -0.03613 0.20124 0.43046
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.12085 0.62257 11.438 8.24e-08 ***
sio2 -0.67250 0.20953 -3.210 0.007498 **
al2o3 0.40534 0.08641 4.691 0.000522 ***
p2o5 -0.76909 0.11103 -6.927 1.59e-05 ***
as.numeric(dus) -0.64020 0.18101 -3.537 0.004094 **
I get quite plausible result which can be interpreted without problems.
My data is a result of designed experiment (more or less :) and therefore
all variables are significant. Is that the reason why scaling may bye
inappropriate in this case?
> Duncan Murdoch
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