get_enviro_cor function

Extract covariances and correlations due to shared environmental responses

Calculates the correlation between columns of the response matrix, due to similarities in the response to explanatory variables i.e., shared environmental response.

get_enviro_cor(mod, type = "mean", prob = 0.95)

Arguments

• mod: An object of class "jSDM"
• type: A choice of either the posterior median (type = "median") or posterior mean (type = "mean"), which are then treated as estimates and the fitted values are calculated from. Default is posterior mean.
• prob: A numeric scalar in the interval $(0,1)$ giving the target probability coverage of the intervals, by which to determine whether the correlations are "significant". Defaults to 0.95.

Returns

results, a list including : - cor, cor.lower, cor.upper: A set of $np x np$ correlation matrices, containing either the posterior median or mean estimate over the MCMC samples plus lower and upper limits of the corresponding 95 % highest posterior interval.

• cor.sig: A $np x np$ correlation matrix containing only the “significant" correlations whose 95 % highest posterior density (HPD) interval does not contain zero. All non-significant correlations are set to zero.

• cov: Average over the MCMC samples of the $np x np$ covariance matrix.

Details

In both independent response and correlated response models, where each of the columns of the response matrix $Y$ are fitted to a set of explanatory variables given by $X$, the covariance between two columns $j$ and $j'$, due to similarities in their response to the model matrix, is thus calculated based on the linear predictors $X \beta_j$ and $X \beta_j'$, where $\beta_j$ are species effects relating to the explanatory variables. Such correlation matrices are discussed and found in Ovaskainen et al., (2010), Pollock et al., (2014).

Examples

library(jSDM)
# frogs data
 data(frogs, package="jSDM")
 # Arranging data
 PA_frogs <- frogs[,4:12]
 # Normalized continuous variables
 Env_frogs <- cbind(scale(frogs[,1]),frogs[,2], 
                    scale(frogs[,3]))
 colnames(Env_frogs) <- colnames(frogs[,1:3])
 Env_frogs <- as.data.frame(Env_frogs)
 # Parameter inference
# Increase the number of iterations to reach MCMC convergence
mod <- jSDM_binomial_probit(# Response variable
                             presence_data=PA_frogs,
                             # Explanatory variables
                             site_formula = ~.,
                             site_data = Env_frogs,
                             n_latent=0,
                             site_effect="random",
                             # Chains
                             burnin=100,
                             mcmc=100,
                             thin=1,
                             # Starting values
                             alpha_start=0,
                             beta_start=0,
                             V_alpha=1,
                             # Priors
                             shape=0.5, rate=0.0005,
                             mu_beta=0, V_beta=10,
                             # Various
                             seed=1234, verbose=1)
# Calcul of residual correlation between species 
 enviro.cors <- get_enviro_cor(mod)

References

Hui FKC (2016). “boral: Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in R.” Methods in Ecology and Evolution, 7, 744–750.

Ovaskainen et al. (2010). Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology, 91, 2514-2521.

Pollock et al. (2014). Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods in Ecology and Evolution, 5, 397-406.

See Also

cov2cor get_residual_cor jSDM-package jSDM_binomial_probit

jSDM_binomial_logit jSDM_poisson_log

Author(s)

Ghislain Vieilledent ghislain.vieilledent@cirad.fr

Jeanne Clément jeanne.clement16@laposte.net