Description Usage Arguments Value Details Author(s) References See also Examples

`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.

1 2 3 4 5 6 7 8 |

`obj` |
object of either class |

`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) |

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.

`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`

package 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.

Gordana Popovic <g.popovic@unsw.edu.au>.

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.

`plot.cgr`

1 2 3 4 5 | ```
X <- as.data.frame(spider$x)
abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = X, ncores=2)
spid_graph=cgr(spider_mod)
plot(spid_graph,pad=1)
``` |

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