MoE_AvePP function

Average posterior probabilities of a fitted MoEClust model

Calculates the per-component average posterior probabilities of a fitted MoEClust model.

MoE_AvePP(x,
          group = TRUE)

Arguments

• x: An object of class "MoEClust" generated by MoE_clust, or an object of class "MoECompare" generated by MoE_compare. Models with gating and/or expert covariates and/or a noise component are facilitated here too.
• group: A logical indicating whether the average posterior probabilities should be computed per component. Defaults to TRUE.

Returns

When group=TRUE, a named vector of numbers, of length equal to the number of components (G), in the range [1/G,1], such that larger values indicate clearer separation of the clusters. Note that G=x$G for models without a noise component and G=x$G + 1 for models with a noise component. When group=FALSE, a single number in the same range is returned.

Details

When group=TRUE, this function calculates AvePP, the average posterior probabilities of membership for each component for the observations assigned to that component via MAP probabilities. Otherwise, an overall measure of clustering certainty is returned.

Note

This function will always return values of 1 for all components for models fitted using the "CEM" algorithm (see MoE_control), or models with only one component.

Examples

data(ais)
res <- MoE_clust(ais[,3:7], G=3, gating= ~ BMI + sex, 
                 modelNames="EEE", network.data=ais)

# Calculate the AvePP per component
MoE_AvePP(res)

# Calculate an overall measure of clustering certainty
MoE_AvePP(res, group=FALSE)

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_control, MoE_entropy

Author(s)

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