MEDseq_AvePP | R Documentation |
Calculates the per-component average posterior probabilities of a fitted MEDseq model.
MEDseq_AvePP(x)
x |
An object of class |
This function calculates AvePP, the average posterior probability of membership for each component for the observations assigned to that component via MAP probabilities.
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.
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.
Keefe Murphy - <keefe.murphy@mu.ie>
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>.
MEDseq_fit
, MEDseq_control
, MEDseq_entropy
# 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 MEDseq_AvePP(mod)
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