View source: R/plot_residual_cor.R
plot_residual_cor | R Documentation |
Plot the posterior mean estimator of residual correlation matrix reordered by first principal component using corrplot
function from the package of the same name.
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,
...
)
mod |
An object of class |
prob |
A numeric scalar in the interval |
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 |
tl.srt |
Numeric, for text label string rotation in degrees, see |
tl.cex |
Numeric, for the size of text label (variable names). |
... |
Further arguments passed to |
No return value. Displays a reordered correlation matrix.
Ghislain Vieilledent <ghislain.vieilledent@cirad.fr>
Jeanne Clément <jeanne.clement16@laposte.net>
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.
corrplot
jSDM-package
jSDM_binomial_probit
jSDM_binomial_logit
jSDM_poisson_log
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)
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