MoE_AvePP: Average posterior probabilities of a fitted MoEClust model

In MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Average posterior probabilities of a fitted MoEClust model

Description

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

Usage

MoE_AvePP(x)

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.

Details

This function calculates AvePP, the average posterior probability of membership for each component for the observations assigned to that component via MAP probabilities.

Value

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.

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.

Author(s)

Keefe Murphy - <[e-mail]>

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_control, MoE_entropy

Examples

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

# Calculate the AvePP
MoE_AvePP(res)

MoEClust documentation built on Dec. 28, 2022, 2:24 a.m.