expert_covar: Account for extra variability in covariance matrices with...
In Keefe-Murphy/MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

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

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 of the means, usually resulting in bigger shapes & sizes for the MVN ellipses. The function also works for univariate response data.

1	expert_covar(x)

`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.

This function is used internally by plot.MoEClust and as.Mclust, for visualisation purposes.

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

The modelName of the resulting variance object may not correspond to the model name of the "MoEClust" object, in particular scale, shape, &/or orientation may no longer be constrained across clusters. Usually, the modelName of the transformed variance object will be "VVV".

Keefe Murphy - <keefe.murphy@mu.ie>

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. <doi: 10.1007/s11634-019-00373-8>.

MoE_clust, MoE_gpairs, plot.MoEClust, as.Mclust

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)