[R-sig-eco] Data transformation prior to RDA

Etienne Laliberté etiennelaliberte at gmail.com
Tue Apr 20 01:48:06 CEST 2010


Are your variables species abundances, or other types of descriptors? If
the former, standardization by column may not be ideal. Transformations
such as the Hellinger, as suggested by Michael, were developed for
species abundances data (Legendre & Gallagher 2001).

There are many ways to transform variables to normalize them, if that's
what you're after; see chapter 1 or Legendre & Legendre (1998). The
Box-Cox method is possibly the closest thing to what you're asking, i.e.
the "best possible transformation for each of the variables". But I'm
convinced there are as many opinions on the subject as there are
different methods.

Cheers

Etienne

Le lundi 19 avril 2010 à 20:02 -0300, Devoto Mariano a écrit :
> Dear all,
> I'm trying to do a redundancy analysis. I'm following Legendre & Legendre's
> (1998) tips to prepare the data prior to the analysis, and Im hoping to do
> the analysis using package 'vegan'.
> I've already centered and standardized my explanatory and response
> variables, but I'm having trouble at deciding whether or not (and how) data
> should be transformed "to linearise the relationships and make the
> distributions more symmetric". Is there a way to find the best possible
> transformation for each variable but considering at the same time its
> linearity to the other ones? Please tell me if I'm not even asking the right
> question here...
> Heres my dataset. First 3 columns are my response variables. All the others
> are explanatory. I know this is a rather basic query, but any tips will be
> greatly appreciated.
> 
>   -0.49350555 -0.37364383  0.70566360 -1.1180986 -1.14255167 -1.30234943
> -1.0812858 -0.4910362
> -1.02769104  0.21678178  1.11781073 -1.1123319 -0.88277150 -0.80445588
> -1.0638291  0.3241891
> -0.64335588 -2.07868376 -1.36782590 -1.0585453 -1.02709382 -1.07710897
> -0.2760976  1.4695121
> 0.25799225  0.82044015  1.02481726 -1.1114373 -0.94050043 -1.23089531
> -0.7064526 -0.5012921
> 0.56048832 -0.29655712 -0.07148828 -1.1099933 -1.17141614  1.54301771
> -1.0921962 -1.9517655
> -0.36443725 -1.49241963 -0.23840793 -1.1180554 -1.14255167 -1.24049362
> -1.0856499 -0.6977804
> -1.97959936  1.30035099 -1.18114614  1.0885061 -0.59412687 -0.21062037
> 1.7890870  0.5018224
> -0.24966043 -0.66228200  0.69101500 -0.8697510 -0.88277150 -0.83963955
> 0.1330428  1.3450534
> 0.24720930  0.35162548 -1.34252630  1.6571129 -0.59412687 -0.13708733
> 2.0090270  0.7553207
> -0.35385550  0.99058254 -1.14295716 -0.6801336 -0.76731365 -0.93148980
> 1.9120456  1.4084094
> -0.92880313  1.14039444  1.38922106 -0.9008538 -0.79617811 -0.96178699
> 0.6512872  1.2365340
> -0.24431565 -0.20947362  0.76084722 -0.8978493 -0.59412687 -0.56565825
> -0.4639991 -0.2045137
> -0.60428104  1.05108295 -0.68704030  1.1833813  0.41612935 -0.07054391
> 1.2816664  0.6181682
> 0.63837128  0.06672464  0.32041910  0.4154816  0.12748471  0.46057549
> -0.2488216  0.3867322
> 0.67144677  0.66889622  1.83857364  0.8375587  0.27180703  0.82551787
> -0.2488216 -0.5987399
> 2.53611774  1.45517653 -0.22337307  0.9253861  0.06975579 -0.22307224
> 1.6332240  0.5146235
> -0.13273765 -0.55628531  0.55154280 -0.2721408  0.99341861 -0.14553291
> -0.1669935  0.9976660
> -0.02043306 -1.52670601 -2.08967318  1.7138916  2.14799715  2.18006143
> -0.6034099 -0.9383742
> 0.80218610 -0.58481301  0.18945796  0.9761855  1.57070788  1.90295452
> -0.6579619 -1.3578423
> 1.32726744  0.64941495 -0.42596631  0.7975236  0.87796076  0.63986198
> -0.0760734 -1.0445683
> -1.53219503  0.57349823  1.03668089  0.5040093  1.05114754  0.83815684
> -0.3852017 -0.8672218
> 0.67016035  0.81036993  0.14519361  0.5065215  1.05114754  0.49360195
> -0.1124414 -0.7921778
> 1.53517131 -0.85469204 -0.12003248  0.3702800  1.02228308  0.66797133
> -0.3185269 -1.1538661
> -0.67154028 -1.45978251 -0.88080583 -0.7266479  0.93568969  0.18901542
> -0.8216180  1.0411473
> 
> Thanks!
> 
> Best wishes,
> 
> Mariano
> 
> --------------------------
> 
> Mariano Devoto
> School of Biological Sciences
> University of Bristol
> Woodland Road
> 
> Bristol, UK
> BS8 1UG
> Tel. +44 (0) 1179545960 (internal 45960)
> web: http://agro.uba.ar/~mdevoto <http://agro.uba.ar/%7Emdevoto>
> 
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> 
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-- 
Etienne Laliberté
================================
School of Forestry
University of Canterbury
Private Bag 4800
Christchurch 8140, New Zealand
Phone: +64 3 366 7001 ext. 8365
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