[R-sig-eco] How to accommodate data with negative values for Canonical Correspondence Analysis in R using "vegan" Package

[Chris Howden]

Just want to clarify some rather lax terminology I used in my previous
post (thanks for pointing this out to me Martin).

Adding the minimum as I suggested does actually change the ratio.

What I was trying to say, very poorly, was that if a 1 point increase in
the predictor is correlated to a 10 point increase in the response then
changing the predictors scale by adding or subtracting a constant
shouldn't change that. So standard predictor regression parameters won't
change, but as the average has changed the intercept will.

Or to put it another way if U plot the predictor and response the pattern
in the plot won't change if U add or subtract a constant from either one
(although the scales will).

I incorrectly used the term "ratio" since I was thinking of the gradient
not changing in the plot and a gradient is kind of like a ratio. So my
apologies if I mislead anyone! (and if someone can think of the correct
scale property I'm trying to convey that hasn't changed I'd appreciate
being told)

Chris Howden B.Sc. (Hons) GStat.
Founding Partner
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling and Training
(mobile) 0410 689 945
(skype) chris.howden
chris at trickysolutions.com.au




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-----Original Message-----
From: Chris Howden [mailto:chris at trickysolutions.com.au]
Sent: Thursday, 27 February 2014 9:22 AM
To: Jari Oksanen
Cc: Ivailo; Rajendra Mohan panda; r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] How to accommodate data with negative values for
Canonical Correspondence Analysis in R using "vegan" Package

Whenever I hit this problem I add the minimum to all scores so they now
range from 0 and above. If necessary I can back transform.

As I haven't changed the ratio the parameters coefficients have the same
meaning, so interpretation is no problem. (Although the intercept may now
change and be difficult to interpret)

This will work if the scale is a human construct where negative and
positive numbers convey the same info, however if they mean different
things it may not work. For example some indices have different meanings
if negative.

Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling and Training

(mobile) 0410 689 945
(fax / office)
chris at trickysolutions.com.au

Disclaimer: The information in this email and any attachments to it are
confidential and may contain legally privileged information. If you are
not the named or intended recipient, please delete this communication and
contact us immediately. Please note you are not authorised to copy, use or
disclose this communication or any attachments without our consent.
Although this email has been checked by anti-virus software, there is a
risk that email messages may be corrupted or infected by viruses or other
interferences. No responsibility is accepted for such interference. Unless
expressly stated, the views of the writer are not those of the company.
Tricky Solutions always does our best to provide accurate forecasts and
analyses based on the data supplied, however it is possible that some
important predictors were not included in the data sent to us. Information
provided by us should not be solely relied upon when making decisions and
clients should use their own judgement.

> On 27 Feb 2014, at 1:39, Jari Oksanen <jari.oksanen at oulu.fi> wrote:
>
> It depends *where* to include negative values. Negative values are OK as
constraints (environmental variables, right hand side of the model
formula). However, all marginal sums of the response data (left hand side
of the model formula) must be above zero. It is technically possible to
have some negative entries in the response matrix as long as the marginal
sums are positive, but you really should not have them. *CA family of
methods were originally developed for non-negative data, and having
negative entries usually indicates that your data are not at all suitable
for the method. If you insist on the analysis, then you really must know
what you are doing, but if you really know, you do not need ask in
R-sig-ecology.
>
> I still repeat: you can have negative data as constraints. If that
fails, then something else is wrong with your data.
>
> If you want to use CCA family of methods for negative response data,
then RDA with equal scaling of variables is usable. Very commonly negative
values also indicate that your response variables were measured in
different scales and units, and therefore you must set scale=TRUE in the
rda() call. Not knowing the data or any other details, this is a blind
watchmaker recommendation.
>
> Cheers, Jari Oksanen
> ________________________________________
> From: r-sig-ecology-bounces at r-project.org
> [r-sig-ecology-bounces at r-project.org] on behalf of Ivailo
> [ubuntero.9161 at gmail.com]
> Sent: 26 February 2014 16:23
> To: Rajendra Mohan panda
> Cc: r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] How to accommodate data with negative values
> for Canonical Correspondence Analysis in R using "vegan" Package
>
> On Wed, Feb 26, 2014 at 2:43 PM, Rajendra Mohan panda
> <rmp.iit.kgp at gmail.com> wrote:
> ...
>> I have temperature data with negative values which I am not able to
>> include for my CCA ordination. ...
>
> Rajendra, I am curious -- why are you not able to include the negative
> values in the CCA ordination?
>
> --
> "The cure for boredom is curiosity. There is no cure for curiosity."
> -- Dorothy Parker
>
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