plot_associations function

plot_associations plot species-species associations

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
)

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

Returns

No return value. Displays species-species associations.

Details

After fitting the jSDM with latent variables, the fullspecies residual correlation matrix : $R=(R_ij)$ with $i=1,..., n_species$ and $j=1,..., n_species$ 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 :

$$R_{ij} = \frac{\Sigma_{ij}}{\sqrt{\Sigma_ii\Sigma _jj}}R_ij = Sigma_ij / sqrt(Sigma_ii.Sigma _jj)$$

.

Examples

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")

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") .

See Also

jSDM-package get_residual_cor

jSDM_binomial_probit jSDM_binomial_probit_long_format

jSDM_binomial_probit_sp_constrained jSDM_binomial_logit jSDM_poisson_log

Author(s)

Ghislain Vieilledent ghislain.vieilledent@cirad.fr

Jeanne Clément jeanne.clement16@laposte.net