Duncan Murdoch <murd...@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 <murd...@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 the > >> 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, if > > > >> you change from Kg to mg, the numbers will become much larger, the > >> variable will contribute much more variance, and it will become a more > >> important part of the largest principal component. Is that sensible? > > > > Basically variables are in percentages (all between 0 and 6%) except dus > > which is present or not present (for the purpose of prcomp transformed to > > 0/1 by as.numeric:). The only variable which is not such is iep which is > > 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 used > > 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 settings > > 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: > > Scaled: > > Standard deviations: > [1] 1.3335175 1.2311551 1.0583667 0.7258295 0.2429397 > > Not Scaled: > > Standard deviations: > [1] 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 different. > 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) > summary(fit) Call: lm(formula = iep ~ sio2 + al2o3 + p2o5 + as.numeric(dus), data = rglp) Residuals: Min 1Q Median 3Q Max -0.41751 -0.15568 -0.03613 0.20124 0.43046 Coefficients: 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? Regards Petr Pikal > > Duncan Murdoch ______________________________________________ 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.