Filter clusters heuristic

Source: R/choose_clusters.R

This removes the clusters from a fit object. There are 2 conditions that can be used to filter out clusters: 1) the cluster size (mixing proportion), which one can require to be above a certain cutoff, and 2) the Binomial peak, whichi one can ask to be above a certain value in at least a certain number of dimensions. Output clusters will be renamed by size (C1 will be larger etc.), and the latent variables and hard clustering assignments will be updated accordingly.

choose_clusters(
  x,
  binomial_cutoff = 0.05,
  dimensions_cutoff = 1,
  pi_cutoff = 0.02,
  re_assign = FALSE
)

Arguments

x

An object of class 'vb_bmm'.

binomial_cutoff

Minimum Binomial success probability.

dimensions_cutoff

Minimum number of dimensions where we want to detect a Binomial component.

pi_cutoff

Minimum size of the mixture component.

re_assign

If TRUE, point assigned to a cluster that is filtered our, are re-assigned from the density function (without updating the parameters). Otherwise, clustering assignments for those points are returned as NA.

Value

An object of class 'vb_bmm'.

Examples

data(fit_mvbmm_example)
choose_clusters(fit_mvbmm_example)
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
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#> Warning: no non-missing arguments to max; returning -Inf
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#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
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#> Warning: no non-missing arguments to max; returning -Inf
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#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
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#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> ✔ Reduced to k = 2 (from 10) selecting VIBER cluster(s) with π > 0.02, and Binomial p > 0.05 in w > 1 dimension(s).
#> ✔ 81 points are now not assigned (cluster = NA) because their cluster has been removed.
#> ── [ VIBER ] mvbmm - Variational fit for Binomial mixtures n = 231 (w = 2 dimens
#> • Clusters: π = 69% [C2] and 31% [C1], with π > 0.
#> • Binomials: θ = <0.5, 0.49> [C2] and <0.25, 0.25> [C1].
#> ℹ Score(s): ELBO = -47073.329. Fit converged in 20 steps, ε = 1e-10.