Independence of vegetation samples
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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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