disclapmix_adaptive: Adaptive fitting

In disclapmix: Discrete Laplace Mixture Inference using the EM Algorithm

Adaptive fitting

Description

A wrapper around 'disclapmix_robust()' that instead of fitting one model for a given number of clusters, fits models until the best model (lowest marginal BIC) is in the interior (with margin 'M') of all number of clusters tried.

Usage

disclapmix_adaptive(x, margin = 5L, criteria = "BIC_marginal", ...)

Arguments

x	Dataset.
margin	Fit models until there is at least this margin
criteria	The slot to chose the best model from (small values indicate better model)
...	Passed on to 'disclapmix_robust()' (and further to 'disclapmix()')

Details

E.g., the best model has 3 clusters and the margin 'M = 5', then this function ensures that models with 1, 2, ..., 3+5 = 8 clusters are fitted. If e.g. then 7 is better than 3, then it continues such that also models with up to 7+5 = 12 clusters are fitted.

Note that models with 1-5 clusters are always fitted.

Value

A list of all 'disclapmix' fits

Examples

data(danes)
db <- as.matrix(danes[rep(1:nrow(danes), danes$n), 1:(ncol(danes)-1)])
fits <- disclapmix_adaptive(db, margin = 5L)
fits
BICs <- sapply(fits, function(x) x$BIC_marginal)
BICs
ks <- sapply(fits, function(x) nrow(x$y)) # Always same as seq_along(fits)
ks
max_k <- max(ks)
best_k <- which.min(BICs)
max_k
best_k
max_k - best_k # = margin = 5

disclapmix documentation built on June 29, 2022, 5:06 p.m.