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

Model based ordination with Gaussian copulas

1 |

`obj` |
object of either class |

`nlv` |
number of latent variables (default = 2, for plotting on a scatterplot) |

`n.samp` |
integer (default = 500), number of sets residuals used for importance sampling (optional, see detail) |

`seed` |
integer (default = NULL), seed for random number generation (optional) |

`loadings`

latent factor loadings
`scores`

latent factor scores
`sigma`

covariance matrix estimated with `nlv`

latent variables
`theta`

precision matrix estimated with `nlv`

latent variables
`BIC`

BIC of estimated model
`logL`

log-likelihood of estimated model

`cord`

is used to fit a Gaussian copula factor analytic 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 `factanal`

function. 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.cord`

1 2 3 4 5 | ```
X <- as.data.frame(spider$x)
abund <- spider$abund
spider_mod <- stackedsdm(abund,~1, data = X, ncores=2)
spid_lv=cord(spider_mod)
plot(spid_lv,biplot = TRUE)
``` |

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