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 I´m 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...
Here´s 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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