ecoCopula (version 1.0.6)

cgr: Fitting Gaussian copula graphical lasso to co-occurrence data

Description

cgr is used to fit a Gaussian copula graphical model to multivariate discrete data, like species co-occurrence data in ecology. This function fits the model and estimates the shrinkage parameter using BIC. Use plot.cgr to plot the resulting graph.

Usage

cgr(
  obj,
  lambda = NULL,
  n.lambda = 100,
  n.samp = 500,
  method = "BIC",
  seed = NULL
)

Value

Three objects are returned; best_graph is a list with parameters for the 'best' graphical model, chosen by the chosen method; all_graphs is a list with likelihood, BIC and AIC for all models along lambda path; obj is the input object.

Arguments

obj

object of either class manyglm, or manyany with ordinal models clm

lambda

vector, values of shrinkage parameter lambda for model selection (optional, see detail)

n.lambda

integer, number of lambda values for model selection (default = 100), ignored if lambda supplied

n.samp

integer (default = 500), number of sets residuals used for importance sampling (optional, see detail)

method

method for selecting shrinkage parameter lambda, either "BIC" (default) or "AIC"

seed

integer (default = 1), seed for random number generation (optional, see detail)

Author(s)

Gordana Popovic <[e-mail]>.

Details

cgr is used to fit a Gaussian copula graphical model to multivariate discrete data, such as co-occurrence (multi species) data in ecology. The model is estimated using importance sampling with n.samp sets of randomised quantile or "Dunn-Smyth" residuals (Dunn & Smyth 1996), and the glasso function for fitting Gaussian graphical models. Models are fit for a path of values of the shrinkage parameter lambda chosen so that both completely dense and sparse models are fit. The lambda value for the best_graph is chosen by BIC (default) or AIC. The seed is controlled so that models with the same data and different predictors can be compared.

References

Dunn, P.K., & Smyth, G.K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 236-244.

Popovic, G. C., Hui, F. K., & Warton, D. I. (2018). A general algorithm for covariance modeling of discrete data. Journal of Multivariate Analysis, 165, 86-100.

See Also

plot.cgr

Examples

# \donttest{
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph,pad=1)
# }