expert_covar function

Account for extra variability in covariance matrices with expert covariates

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.

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

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.

Returns

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

Details

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

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, and cholsigma, if it was in the input, will be discarded from the output. Usually, the modelName of the transformed variance object will be "VVV" for multivariate data and "V" for univariate data, but not always. Furthermore, the output will drop certain row and column names from the result.

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)

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

Author(s)

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