Using multiple species data for gam
Hello, not sure if you are looking to run the GLM/GAMs individually but in one run, or as a community composition type model to test main drivers/correlates of combined species occurrences. If the latter, another option is a GLMM with species having random slope to allow responses to differ. For this, you would need to stack the occurrence matrix into a ?long? format (a row for the presence/absence of each species in each plot with corresponding predictor variables and a field for species). Response Species Temp Pptn 0 Sp1 30 1000 1 Sp2 30 1000 1 Sp3 30 1000 In lme4, something like: lmer(Response ~ Temp + Pptn + (1 + Temp + Pptn|Species), family=binomial(link="logit"), data) An example with R code in the Appendix: http://dx.doi.org/10.1111/jvs.12111 Greg -- Dr Greg Guerin Postdoctoral Fellow School of Biological Sciences, Faculty of Science The University of Adelaide CRICOS Provider Number 00123M ----------------------------------------------------------- IMPORTANT: This message may contain confidential or legally privileged information. If you think it was sent to you by mistake, please delete all copies and advise the sender. For the purposes of the SPAM Act 2003, this email is authorised by The University of Adelaide.
---------------------------------------------------------------------- Message: 1 Date: Tue, 10 Feb 2015 09:28:14 -0700 From: Tim Meehan <tmeeha at gmail.com> To: Rajendra Mohan panda <rmp.iit.kgp at gmail.com> Cc: "r-sig-ecology at r-project.org" <r-sig-ecology at r-project.org> Subject: Re: [R-sig-eco] Using multiple species data for gam Message-ID: <CAMTWOzpv58RRX2ocgTCpXh1EPAcxzkSCGKHvdUsaytGhCJH8MQ at mail.gmail.com> Content-Type: text/plain; charset="UTF-8" If you want to do this in a glm framework, you might look into the mvabund package: http://cran.r-project.org/web/packages/mvabund/mvabund.pdf I've never used it with anything approaching 1000 species, though. On Tue, Feb 10, 2015 at 2:41 AM, Rajendra Mohan panda <rmp.iit.kgp at gmail.com
wrote:
Dear All
I have >1000 species with presence and absence (0 or 1) values and with
seven corresponding predictor variables. If I can run gam/glm for the
data
using all species data simultaneously vs predictors. Data are arranged
in
columns against their GPS locations (see below). I know it is possible
to
do separately for each species.
Your kind response is highly appreciated.
Sites Sp1 Sp2 Sp3 Alt Temp Pptn Ft
1A 0 1 1 20 30 1000 Evergreen
With Best Regards
Rajendra M Panda
School of Water Resources
Indian Institute of Technology Kharagpur, India
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