MoEClust (version 1.6.0)

expert_covar: Account for extra variability in covariance matrices with expert covariates

Description

In the presence of expert network covariates, this helper function modifies the component-specific covariance matrices of a "MoEClust" object, in order to account for the extra variability due to the component means, usually resulting in bigger shapes & sizes for the MVN ellipses in MoE_gpairs plots. The function also works for univariate response data.

Usage

expert_covar(x,
             weighted = TRUE,
             ...)

Value

The variance component only from the parameters list from the output of a call to MoE_clust, modified accordingly.

Arguments

x

An object of class "MoEClust" generated by MoE_clust, or an object of class "MoECompare" generated by MoE_compare. Models with a noise component are facilitated here too.

weighted

A logical indicating whether the estimated cluster membership probabilities should be used to provide a weighted estimate of the variability due to the component means. Defaults to TRUE. The option weighted=FALSE is provided only so that previous behaviour under earlier versions of MoEClust can be recovered but is otherwise not recommended.

...

Catches unused arguments.

Author

Keefe Murphy - <>

Details

This function is used internally by MoE_gpairs, plot.MoEClust(x, what="gpairs"), and as.Mclust, for visualisation purposes.

References

Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. Advances in Data Analysis and Classification, 14(2): 293-325. <tools:::Rd_expr_doi("10.1007/s11634-019-00373-8")>.

See Also

MoE_clust, MoE_gpairs, plot.MoEClust, as.Mclust

Examples

data(ais)
res   <- MoE_clust(ais[,3:7], G=2, gating= ~ 1, expert= ~ sex,
                   network.data=ais, modelNames="EEE", equalPro=TRUE)

# Extract the variance object
res$parameters$variance

# Modify the variance object
expert_covar(res)