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-requ...@r-project.org wrote:
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Today's Topics:
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....@gmail.com>
To: r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] Using multiple species data for gam
Message-ID:
<cagtzhju7-0nfemukr_wcgt9ic2vhtcl926xsa9s1od1-gcq...@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....@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.war...@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]]
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R-sig-ecology@r-project.org
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------------------------------
Message: 2
Date: Wed, 2 Sep 2015 22:08:37 +0530
From: Rajendra Mohan Panda <rmp.iit....@gmail.com>
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco] Fwd: Using multiple species data for gam
Message-ID:
<cagtzhjuwcrvq1hk7k4aazfa8qplm4nukf_unydunx9gqsvc...@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....@gmail.com>
Date: 2 September 2015 at 18:08
Subject: Re: [R-sig-eco] Using multiple species data for gam
To: r-sig-ecology@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....@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.war...@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@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 <abedim...@gmail.com>
To: "<r-sig-ecology@r-project.org>" <r-sig-ecology@r-project.org>
Subject: [R-sig-eco] comparision of lsmean and significant interaction
Message-ID:
<cadghagigtudmpkitfranurnpjbfg-57xuwe_b4zko6mubjy...@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
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