Using GLMs or GLMMs for diversity metrics?
Hi everyone In case it's helpful, Instats is offering a new 3-day seminar on VGLMs and VGAMs, which can easily handle the case of non-normal distributions and nonlinear relationships among predictors and outcomes. The vectorized approach allows for multivariate outcomes being modeled simultaneously. https://instats.org/seminar/vector-generalized-linear-and-additive-m3 The seminar uses the VGAM package in R, and the instructor Thomas Yee is the chief architect of the innovative VGLM/VGAM approach. Best wishes Michael Zyphur Director Institute for Statistical and Data Science *instats.org* <http://instats.org>
On Mon, 14 Oct 2024 at 05:10, Manuel Sp?nola <mspinola10 at gmail.com> wrote:
Also you can try the R package spAbundance that allow you to model detection probabilty and also Random effects. Manuel *Manuel Sp?nola, Ph.D.* Instituto Internacional en Conservaci?n y Manejo de Vida Silvestre Universidad Nacional Apartado 1350-3000 Heredia COSTA RICA mspinola at una.cr <mspinola at una.ac.cr> mspinola10 at gmail.com Tel?fono: (506) 8706 - 4662 Sitio web institucional: ICOMVIS <http://www.icomvis.una.ac.cr/index.php/manuel> Sitio web personal: Sitio personal <https://mspinola-sitioweb.netlify.app> Blog sobre Ciencia de Datos: Blog de Ciencia de Datos <https://mspinola-ciencia-de-datos.netlify.app> El El vie, 11 oct 2024 a la(s) 16:26, Barton, Alana Charlotte via R-sig-ecology <r-sig-ecology at r-project.org> escribi?:
Hello, I would appreciate some help in a question regarding statistical
analysis.
I'm looking at species count data where sampling was carried out over multiple years in repeated sites. So each year was sampled at six
different
sites for example. The years were categorized into a temperature group
with
two factors:warm or cold. However, I'm only interested in exploring community differences between temp. groups and across years. I used the vegan package in R for calculating diversity metrics(abundance, richness, diversity index, evenness) and want to statistically check differences among metrics from factors of group and year. I have been using the manyglm-mvabund package with negative binomial distribution, but there is the issue that mvabund doesn't fit non-integer data well, and I'm worried its incorrectly computing diversity and
evenness
stats. Additionally, I'm wondering if the repeated sites should be added as a fixed effect to mitigate this? Or if it's even considered a random effect actually and a mixed model is more appropriate, using
glmmTMB
instead in this case? I'm not terribly familiar with using mixed models
in
R so any help is appreciated.
Thank you for your help
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