We would like to announce the following online statistics course:
Online course: Introduction to GLLVM and multivariate GLMM
When: *2 - 5 December, 09.00-16.00 UK* time OR *9 - 12 December,
15.00-21.00 UK time (10.00-16.00 EST) *
Website: https://www.highstat.com/
The central theme of this course is the analysis of multiple correlated
response (or dependent) variables using GLMs and GLMMs. Rather
than applying multiple univariate GLMs or GLMMs, we will focus on
multivariate GLMMs, particularly generalised linear latent variable
models (GLLVMs), for the simultaneous analysis of all variables.
During the course, we cover a large number of exercises with examples
such as trait variables from turtle hatchlings from multiple clutches,
biomass data from fish species sampled at multiple sites, count data
from 250 freshwater benthic species sampled at 200 sites, abundances
of multiple parasite species on fish, counts of 60 different debris
types in water samples, abundances of multiple spider species in traps,
multiple
morphometric variables sampled from honeybees, and absence/presence of
diet variables from faecal samples of brown bears.
In all these examples, we can analyse each variable with a univariate
GLM(M). Although these analyses are relatively simple, there are also
some problems:
* Extra Work: Individual analyses are computationally less efficient
and require separate validation, interpretation, and reporting.
* Lack of Multivariate Relationships: Analysing the variables
individually neglects the interconnected relationships and
interactions between them.
* No Shared Variation: Univariate models might overlook consistent
residual patterns across species, while multivariate models can
capture shared variations due to common environmental factors.
* Multiple Testing: Conducting separate analyses increases the risk of
Type I errors, especially when the response variables are highly
correlated.
* Loss of Community-Level Insights: Analysing species separately
misses out on a comprehensive, community-level viewpoint and can
lead to inconsistent conclusions.
Kind regards,
Alain
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