MoE_AvePP | R Documentation |
Calculates the per-component average posterior probabilities of a fitted MoEClust model.
MoE_AvePP(x,
group = TRUE)
x |
An object of class |
group |
A logical indicating whether the average posterior probabilities should be computed per component. Defaults to |
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.
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.
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.
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. <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11634-019-00373-8")}>.
MoE_clust
, MoE_control
, MoE_entropy
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)
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