MEDseq_AvePP {MEDseq}

Average posterior probabilities of a fitted MEDseq model

Description

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

Usage

MEDseq_AvePP(x,
             group = TRUE)

Arguments

x

An object of class "MEDseq" generated by MEDseq_fit or an object of class "MEDseqCompare" generated by MEDseq_compare.

group

A logical indicating whether the average posterior probabilities should be computed per component. Defaults to TRUE.

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.

Value

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. When group=FALSE, a single number in the same range is returned.

Note

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

Author(s)

Keefe Murphy - <[e-mail]>

References

Murphy, K., Murphy, T. B., Piccarreta, R., and Gormley, I. C. (2021). Clustering longitudinal life-course sequences using mixtures of exponential-distance models. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(4): 1414-1451. <doi:10.1111/rssa.12712>.

See Also

MEDseq_fit, MEDseq_control, MEDseq_entropy

Examples

# Load the MVAD data
data(mvad)
mvad$Location <- factor(apply(mvad[,5:9], 1L, function(x) 
                 which(x == "yes")), labels = colnames(mvad[,5:9]))
mvad          <- list(covariates = mvad[c(3:4,10:14,87)],
                      sequences = mvad[,15:86], 
                      weights = mvad[,2])
mvad.cov      <- mvad$covariates

# Create a state sequence object with the first two (summer) time points removed
states        <- c("EM", "FE", "HE", "JL", "SC", "TR")
labels        <- c("Employment", "Further Education", "Higher Education", 
                   "Joblessness", "School", "Training")
mvad.seq      <- seqdef(mvad$sequences[-c(1,2)], states=states, labels=labels)

# Fit a model with weights and a gating covariate
# Have the probability of noise-component membership be constant
mod           <- MEDseq_fit(mvad.seq, G=11, modtype="UUN", weights=mvad$weights, 
                            gating=~ gcse5eq, covars=mvad.cov, noise.gate=FALSE)

# Calculate the AvePP per component
MEDseq_AvePP(mod)

# Calculte an overall measure of clustering certainty
MEDseq_AvePP(mod, group=FALSE)