plot_associations: plot_associations plot species-species associations

Description

plot_associations plot species-species associations

Usage

plot_associations(
  R,
  radius = 5,
  main = NULL,
  cex.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
)

Value

No return value. Displays species-species associations.

Arguments

R

matrix of correlation $R$

radius

circle's radius

main

title

cex.main

title's character size. NULL and NA are equivalent to 1.0.

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 : $\Sigma_{ij}=\lambda_i' .\lambda_j$, in the case of a regression with probit, logit or poisson link function and

$\Sigma_{ij}$	$= \lambda_i' .\lambda_j + V$	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 tools:::Rd_expr_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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\(\Sigma_{ij}\)	\(= \lambda_i' .\lambda_j + V\)	if i=j
	\(= \lambda_i' .\lambda_j\)	else,