[R-sig-eco] Independence of vegetation samples

[Michael Marsh]

Is there a method in R for testing for independence of vegetation 
samples, for example because of relative proximity of different samples? 
I would like to treat the 3 radially arranged transects of Jornada Line 
Point Index plots as different sample units.
Mike Marsh
Washington Native Plant Society

On 9/3/2015 3:00 AM, r-sig-ecology-request at r-project.org wrote:
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> Today's Topics:
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>     1. Re: Using multiple species data for gam (Rajendra Mohan Panda)
>     2. Fwd:  Using multiple species data for gam (Rajendra Mohan Panda)
>     3. comparision of lsmean and significant interaction (Mehdi Abedi)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 2 Sep 2015 18:08:16 +0530
> From: Rajendra Mohan Panda <rmp.iit.kgp at gmail.com>
> To: r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] Using multiple species data for gam
> Message-ID:
> 	<CAGTzHJu7-0NfEMukR_WcGt9iC2VHtcL926XSA9s1oD1-GCQHoQ at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Dear All
>
> I find it difficult to run VGAM and MARS for multi-response data. In both
> the models, I get an error message "variable names are limited to 10000
> bytes". Is this due to my big data structure or else ? For your kind
> information, I have 1500 spp. on 434 site locations, and I want to see the
> impact of environment on community structure. I have to analyse how the
> Western Himalaya community behaviour differ from the Eastern Himalaya.
>
> I have been struggling to accommodate my data for model fitting since long,
> could you please give some insights on my idea and how can I tackle the
> error for successful model run.
>
> I always appreciate your valuable advise.
>
>
> Best Regards
> Rajendra M Panda
> School of Water Resources
> Indian Institute of Technology Kharagpur
>
> On 18 February 2015 at 09:32, Rajendra Mohan panda <rmp.iit.kgp at gmail.com>
> wrote:
>
>> Dear Prof David Warton
>>
>> Thanks a lot for your nice introspection on my data. I appreciate your
>> valuable comments. I am also trying to explore gamm or VGAM to match its
>> suitability with data. Its fine. However, I am thinking to reduce my data
>> structure by removing some of the species showing interspecific
>> correlation. Honestly speaking I do not have thought of it. Can you please
>> give more insights regarding this (interspecies correlation). I am also
>> interested in studying species-environment relationship (not by CCA or RDA).
>>
>> Your kind comments are highly appreciated.
>>
>>
>> With Best Regards
>> Rajendra M Panda
>> School of Water Resources
>> Indian Institute of Technology Kharagpur, India
>>
>> On Wed, Feb 18, 2015 at 4:36 AM, David Warton <david.warton at unsw.edu.au>
>> wrote:
>>
>>> Hi Rajendra and Greg,
>>> A couple of quick thoughts:
>>>
>>> Firstly, Rajendra the method that is applicable to your data really
>>> depends on the research question - what is it that you are trying to
>>> achieve.  It is always hard to offer help on what analysis method is suited
>>> to a question without knowing the original research objective.  The gamm
>>> function for example might be useful to you if you are primarily interested
>>> in predictive modelling, and also if you think that you have a common
>>> nonlinear response to environmental variables with some "noise" around this
>>> pattern for different spp (which can be represented as random effects).
>>> You could alternatively use this function to fit a separate smoother for
>>> each spp but that would be a pretty complicated model and few would have
>>> sufficient data to justify that level of model complexity.  VGAM y Thomas
>>> Yee offers and option in between these two.
>>>
>>> Secondly, something you need to worry about with this type of data is
>>> interspecies correlation - for various reasons (including species
>>> interaction), it is widely thought and even better often observed that
>>> species are correlated in abundance (or presence/absence, whatever) even
>>> after accounting for environmental predictors.  This makes the problem
>>> multivariate.  If you care about making joint inferences across species and
>>> you don't account for correlation between species you can get things quite
>>> wrong.  The gamm function I think could handle residual correlation, but
>>> not the way you specified it, and it would have a lot of trouble, unless
>>> you have only a handful of species and quite decent abundance data on
>>> each.  On the other hand if you are just making predictions separately for
>>> each spp then you don't need to worry too much about this.
>>>
>>> All the best
>>> David
>>>
>>>
>>> David Warton
>>> Professor and Australian Research Council Future Fellow
>>> School of Mathematics and Statistics and the Evolution & Ecology Research
>>> Centre
>>> The University of New South Wales NSW 2052 AUSTRALIA
>>> phone (61)(2) 9385-7031
>>> fax (61)(2) 9385-7123
>>>
>>> http://www.eco-stats.unsw.edu.au/ecostats15.html
>>>
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-ecology mailing list
>>> R-sig-ecology at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>>>
>>
> 	[[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 2
> Date: Wed, 2 Sep 2015 22:08:37 +0530
> From: Rajendra Mohan Panda <rmp.iit.kgp at gmail.com>
> To: r-sig-ecology at r-project.org
> Subject: [R-sig-eco] Fwd:  Using multiple species data for gam
> Message-ID:
> 	<CAGTzHJuwCrVQ1Hk7K4aaZfa8qpLm4Nukf_unYduNx9GQSVCjmQ at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> I regret that the error message was due to my  erroneous data. However, I
> face another error message in VGAM run i.e., object "eta" not found. Kindly
> explain why this happens and possible solutions for this.
>
> Thanks in advance
>
> Best Regards
> Rajendra M Panda
> School of Water Resources
> Indian Institute of Technology Kharagpur
>
> ---------- Forwarded message ----------
> From: Rajendra Mohan Panda <rmp.iit.kgp at gmail.com>
> Date: 2 September 2015 at 18:08
> Subject: Re: [R-sig-eco] Using multiple species data for gam
> To: r-sig-ecology at r-project.org
>
>
> Dear All
>
> I find it difficult to run VGAM and MARS for multi-response data. In both
> the models, I get an error message "variable names are limited to 10000
> bytes". Is this due to my big data structure or else ? For your kind
> information, I have 1500 spp. on 434 site locations, and I want to see the
> impact of environment on community structure. I have to analyse how the
> Western Himalaya community behaviour differ from the Eastern Himalaya.
>
> I have been struggling to accommodate my data for model fitting since long,
> could you please give some insights on my idea and how can I tackle the
> error for successful model run.
>
> I always appreciate your valuable advise.
>
>
> Best Regards
> Rajendra M Panda
> School of Water Resources
> Indian Institute of Technology Kharagpur
>
> On 18 February 2015 at 09:32, Rajendra Mohan panda <rmp.iit.kgp at gmail.com>
> wrote:
>
>> Dear Prof David Warton
>>
>> Thanks a lot for your nice introspection on my data. I appreciate your
>> valuable comments. I am also trying to explore gamm or VGAM to match its
>> suitability with data. Its fine. However, I am thinking to reduce my data
>> structure by removing some of the species showing interspecific
>> correlation. Honestly speaking I do not have thought of it. Can you please
>> give more insights regarding this (interspecies correlation). I am also
>> interested in studying species-environment relationship (not by CCA or RDA).
>>
>> Your kind comments are highly appreciated.
>>
>>
>> With Best Regards
>> Rajendra M Panda
>> School of Water Resources
>> Indian Institute of Technology Kharagpur, India
>>
>> On Wed, Feb 18, 2015 at 4:36 AM, David Warton <david.warton at unsw.edu.au>
>> wrote:
>>
>>> Hi Rajendra and Greg,
>>> A couple of quick thoughts:
>>>
>>> Firstly, Rajendra the method that is applicable to your data really
>>> depends on the research question - what is it that you are trying to
>>> achieve.  It is always hard to offer help on what analysis method is suited
>>> to a question without knowing the original research objective.  The gamm
>>> function for example might be useful to you if you are primarily interested
>>> in predictive modelling, and also if you think that you have a common
>>> nonlinear response to environmental variables with some "noise" around this
>>> pattern for different spp (which can be represented as random effects).
>>> You could alternatively use this function to fit a separate smoother for
>>> each spp but that would be a pretty complicated model and few would have
>>> sufficient data to justify that level of model complexity.  VGAM y Thomas
>>> Yee offers and option in between these two.
>>>
>>> Secondly, something you need to worry about with this type of data is
>>> interspecies correlation - for various reasons (including species
>>> interaction), it is widely thought and even better often observed that
>>> species are correlated in abundance (or presence/absence, whatever) even
>>> after accounting for environmental predictors.  This makes the problem
>>> multivariate.  If you care about making joint inferences across species and
>>> you don't account for correlation between species you can get things quite
>>> wrong.  The gamm function I think could handle residual correlation, but
>>> not the way you specified it, and it would have a lot of trouble, unless
>>> you have only a handful of species and quite decent abundance data on
>>> each.  On the other hand if you are just making predictions separately for
>>> each spp then you don't need to worry too much about this.
>>>
>>> All the best
>>> David
>>>
>>>
>>> David Warton
>>> Professor and Australian Research Council Future Fellow
>>> School of Mathematics and Statistics and the Evolution & Ecology Research
>>> Centre
>>> The University of New South Wales NSW 2052 AUSTRALIA
>>> phone (61)(2) 9385-7031
>>> fax (61)(2) 9385-7123
>>>
>>> http://www.eco-stats.unsw.edu.au/ecostats15.html
>>>
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-ecology mailing list
>>> R-sig-ecology at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>>>
>>
> 	[[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 3
> Date: Thu, 3 Sep 2015 00:43:48 +0430
> From: Mehdi Abedi <abedimail at gmail.com>
> To: "<r-sig-ecology at r-project.org>" <r-sig-ecology at r-project.org>
> Subject: [R-sig-eco] comparision of lsmean and significant interaction
> Message-ID:
> 	<CADGhagiGTUDMpkiTfranUrnpJBfg-57XuWE_B4ZKO6MubjyCgg at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Dear list,
> I have a basic and may simple question.
> When we have two- way or three-way ANOVA or also GLM and in the following
> doing compare lsmean it looks some times complicated.
>
> What should we consider in the case of significant or non significant
> interactions? What is the best strategy to have correct mean comparison?
>
> Warm regards,
> Mehdi
>