expert_covar: Account for extra variability in covariance matrices with...

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

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

Usage

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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.

Details

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

Value

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

Note

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".

Author(s)

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

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

See Also

MoE_clust, MoE_gpairs, plot.MoEClust, as.Mclust

Examples

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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)

Keefe-Murphy/MoEClust documentation built on Jan. 11, 2021, 6:34 p.m.