plot_associations: plot_associations plot species-species associations

Description

plot_associations plot species-species associations

Usage

plot_associations(
  R,
  radius = 5,
  main = NULL,
  circleBreak = FALSE,
  top = 10L,
  occ = NULL,
  env_effect = NULL,
  cols_association = c("#FF0000", "#BF003F", "#7F007F", "#3F00BF", "#0000FF"),
  cols_occurrence = c("#BEBEBE", "#8E8E8E", "#5F5F5F", "#2F2F2F", "#000000"),
  cols_env_effect = c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02",
    "#A6761D", "#666666"),
  lwd_occurrence = 1,
  species_order = "abundance",
  species_indices = NULL
)

Arguments

R

matrix of correlation $R$

radius

circle's radius

main

title

circleBreak

circle break or not

top

number of top negative and positive associations to consider

occ

species occurence data

env_effect

environmental species effects $\beta$

cols_association

color gradient for association lines

cols_occurrence

color gradient for species

cols_env_effect

color gradient for environmental effect

lwd_occurrence

lwd for occurrence lines

species_order

order species according to :

`"abundance"`	their mean abundance at sites by default)
`"frequency"`	the number of sites where they occur
`"main environmental effect"`	their most important environmental coefficients

species_indices

indices for sorting species

Author

Ghislain Vieilledent <ghislain.vieilledent@cirad.fr>

Jeanne Clément <jeanne.clement16@laposte.net>

Details

After fitting the jSDM with latent variables, the fullspecies residual correlation matrix : $R=(R_{ij})$ with $i=1,\ldots, n_{species}$ and $j=1,\ldots, n_{species}$ can be derived from the covariance in the latent variables such as : can be derived from the covariance in the latent variables such as :

$\Sigma_{ij}$	$= \lambda_i .\lambda_j' + 1$	if i=j
	$= \lambda_i .\lambda_j'$	else,

this function represents the correlations computed from covariances : $$R_{ij} = \frac{\Sigma_{ij}}{\sqrt{\Sigma_ii\Sigma _jj}}$$.

References

Pichler M. and Hartig F. (2021) A new method for faster and more accurate inference of species associations from big community data.
Methods in Ecology and Evolution, 12, 2159-2173 (doi:10.1111/2041-210X.13687).

Examples

Run this code

library(jSDM)
# frogs data
data(mites, package="jSDM")
# Arranging data
PA_mites <- mites[,1:35]
# Normalized continuous variables
Env_mites <- cbind(mites[,36:38], scale(mites[,39:40]))
colnames(Env_mites) <- colnames(mites[,36:40])
Env_mites <- as.data.frame(Env_mites)
# Parameter inference
# Increase the number of iterations to reach MCMC convergence
mod <- jSDM_poisson_log(# Response variable
                        count_data=PA_mites,
                        # Explanatory variables
                        site_formula = ~  water + topo + density,
                        site_data = Env_mites,
                        n_latent=2,
                        site_effect="random",
                        # Chains
                        burnin=100,
                        mcmc=100,
                        thin=1,
                        # Starting values
                        alpha_start=0,
                        beta_start=0,
                        lambda_start=0,
                        W_start=0,
                        V_alpha=1,
                        # Priors
                        shape=0.5, rate=0.0005,
                        mu_beta=0, V_beta=10,
                        mu_lambda=0, V_lambda=10,
                        # Various
                        seed=1234, verbose=1)
# Calcul of residual correlation between species
R <- get_residual_cor(mod)$cor.mean
plot_associations(R, circleBreak = TRUE, occ = PA_mites, species_order="abundance")
# Average of MCMC samples of species enrironmental effect beta except the intercept
env_effect <- t(sapply(mod$mcmc.sp,
                       colMeans)[grep("beta_", colnames(mod$mcmc.sp[[1]]))[-1],])
colnames(env_effect) <-  gsub("beta_", "", colnames(env_effect))
plot_associations(R, env_effect = env_effect, species_order="main env_effect")

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\(\Sigma_{ij}\)	\(= \lambda_i .\lambda_j' + 1\)	if i=j
	\(= \lambda_i .\lambda_j'\)	else,