In greta: Simple and Scalable Statistical Modelling in R

Multiple species with partial pooling of regression coefficients

An example of a logistic regression being used to estimate the probability of multiple species' presences along a number of environmental gradients. Instead of assuming independence of species regression coefficients, we assume they are drawn from a shared distribution. We partially pool species responses. This gives us not ony the regression coefficients for each species but also a global average coefficient and a measure of variation between species responses to environmental gradients.

data

# make fake data
n_species <- 5
n_env <- 1
n_sites <- 50

env <- matrix(rnorm(n_sites * n_env), nrow = n_sites)
occupancy <- matrix(rbinom(n_sites * n_species, 1, 0.5), nrow = n_sites)

greta code

global_alpha <- normal(0, 10, dim = 1)
global_alpha_sd <- uniform(0, 10, dim = 1) 
alpha <- normal(global_alpha, global_alpha_sd, dim = n_species)

global_betas <- normal(0, 10, dim = n_env)
global_betas_sd <- uniform(0, 10, dim = n_env)
beta <- normal(global_betas, global_betas_sd, dim = c(n_env, n_species))

env_effect <- env %*% beta

# add intercepts for all species
linear_predictor <- sweep(env_effect, 2, alpha, FUN = '+')

# ilogit of linear predictor
p <- ilogit(linear_predictor)

distribution(occupancy) <- bernoulli(p)