[R-sig-eco] (no subject)

Tue Apr 20 18:10:02 CEST 2010

Hi,
Just to add a note to the good advice you have gotten so far. Redundancy
Aanalysis (RA) is a linear
method, as Jari explained. If you apply transformations until your data 
conform to the assumptions of RA and then do RA, you are no longer
applying a linear method.
You will get back a configuration of your transformed data, but what
does that imply for the original data?
I would work with the untransformed data first and look at the implied
configuration using 
RA. IF that does not capture the features of the data, then start
looking at what does not conform to the 
model. You can look at the VGAM package, Thomas Yee has some ordination
methods, that assume truly
Gaussian (or other distributions) along the principle axis. The key here
is that unlike machine learning methods where only the prediction
accuracy really matters, here you are truly interested in understanding
the underlying structure of your data. Hence you should be wary of
transformation if the underlying deformation of the data configuration
is not well understood.

My 2c
Nicholas

> ------------------------------
> 
> Message: 2
> Date: Mon, 19 Apr 2010 20:02:35 -0300
> From: Devoto Mariano <mdevoto at agro.uba.ar>
> To: r-sig-ecology at r-project.org
> Subject: [R-sig-eco] Data transformation prior to RDA
> Message-ID:
> 	<g2oe08bfeb81004191602o6ff4a09ax995c0fb46984c30d at mail.gmail.com>
> Content-Type: text/plain
> 
> 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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> 
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