plot_residual_cor: Plot the residual correlation matrix from a latent variable...

In jSDM: Joint Species Distribution Models

Plot the residual correlation matrix from a latent variable model (LVM).

Description

Plot the posterior mean estimator of residual correlation matrix reordered by first principal component using corrplot function from the package of the same name.

Usage

plot_residual_cor(
  mod,
  prob = NULL,
  main = "Residual Correlation Matrix from LVM",
  cex.main = 1.5,
  diag = FALSE,
  type = "lower",
  method = "color",
  mar = c(1, 1, 3, 1),
  tl.srt = 45,
  tl.cex = 0.5,
  ...
)

Arguments

mod	An object of class "jSDM".
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". If prob=0.95 is specified only significant correlations, whose 95\% HPD interval does not contain zero, are represented. Defaults to prob=NULL to represent all correlations significant or not.
main	Character, title of the graph.
cex.main	Numeric, title's size.
diag	Logical, whether display the correlation coefficients on the principal diagonal.
type	Character, "full" (default), "upper" or "lower", display full matrix, lower triangular or upper triangular matrix.
method	Character, the visualization method of correlation matrix to be used. Currently, it supports seven methods, named "circle" (default), "square", "ellipse", "number", "pie", "shade" and "color".
mar	See par
tl.srt	Numeric, for text label string rotation in degrees, see text.
tl.cex	Numeric, for the size of text label (variable names).
...	Further arguments passed to corrplot function

Value

No return value. Displays a reordered correlation matrix.

Author(s)

Ghislain Vieilledent <[e-mail]>

Jeanne Clément <[e-mail]>

References

Taiyun Wei and Viliam Simko (2017). R package "corrplot": Visualization of a Correlation Matrix (Version 0.84)

Warton, D. I.; Blanchet, F. G.; O'Hara, R. B.; O'Hara, R. B.; Ovaskainen, O.; Taskinen, S.; Walker, S. C. and Hui, F. K. C. (2015) So Many Variables: Joint Modeling in Community Ecology. Trends in Ecology & Evolution, 30, 766-779.

See Also

corrplot jSDM-package jSDM_binomial_probit
jSDM_binomial_logit jSDM_poisson_log

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])
# 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=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=0.1, rate=0.1,
                           mu_beta=0, V_beta=1,
                           mu_lambda=0, V_lambda=1,
                           # Various
                           seed=1234, verbose=1)
# Representation of residual correlation between species
plot_residual_cor(mod)
plot_residual_cor(mod, prob=0.95)

jSDM documentation built on July 26, 2023, 5:21 p.m.