MoE_entropy {MoEClust} | R Documentation |
Calculates the normalised entropy of a fitted MoEClust model.
MoE_entropy(x)
x |
An object of class |
This function calculates the normalised entropy via
H=-\frac{1}{n\log(G)}\sum_{i=1}^n\sum_{g=1}^G\hat{z}_{ig}\log(\hat{z}_{ig}),
where n
and G
are the sample size and number of components, respectively, and \hat{z}_{ig}
is the estimated posterior probability at convergence that observation i
belongs to component g
. Note that G=x$G
for models without a noise component and G=x$G + 1
for models with a noise component.
A single number, given by 1-H
, in the range [0,1], such that larger values indicate clearer separation of the clusters.
This function will always return a normalised entropy of 1
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. <doi:10.1007/s11634-019-00373-8>.
MoE_clust
, MoE_control
, MoE_AvePP
data(ais)
res <- MoE_clust(ais[,3:7], G=3, gating= ~ BMI + sex,
modelNames="EEE", network.data=ais)
# Calculate the normalised entropy
MoE_entropy(res)