This function can filter out the clusters computed by MOBSTER based on two criteria: the mixing proportion value, the number of mutations assigned and the variance of the Beta clusters.
For all criteria a scalar should be given as input. The return object will contain only the clusters that pass all filters. If any cluster is dropped the latent variables are re-computed, as well as the clustering assignments and the mixing proportions (all mutations will be still assigned after clusters' removal).
Usage
choose_clusters(
x,
pi_cutoff = 0.02,
N_cutoff = 10,
Beta_variance_cutoff = 1e-04,
verbose = FALSE
)Value
A MOBSTER fit object where clusters are larger than pi_cutoff and contain
at least N_cutoff. If no such cluster exists an error is generated.
Examples
data('fit_example', package = 'mobster')
# Does not change anything (no filter triggered)
choose_clusters(fit_example$best)
#> ── [ MOBSTER ] My MOBSTER model n = 5000 with k = 2 Beta(s) and a tail ─────────
#> ● Clusters: π = 55% [C1], 31% [Tail], and 14% [C2], with π > 0.
#> ● Tail [n = 1370, 31%] with alpha = 1.2.
#> ● Beta C1 [n = 2784, 55%] with mean = 0.48.
#> ● Beta C2 [n = 846, 14%] with mean = 0.15.
#> ℹ Score(s): NLL = -5671.5; ICL = -10359.09 (-11266.35), H = 907.26 (0). Fit
#> converged by MM in 75 steps.
# Remove one Beta component because it has less than 100 points (renders the fit very poor)
choose_clusters(fit_example$best, N_cutoff = 100)
#> ── [ MOBSTER ] My MOBSTER model n = 5000 with k = 2 Beta(s) and a tail ─────────
#> ● Clusters: π = 55% [C1], 31% [Tail], and 14% [C2], with π > 0.
#> ● Tail [n = 1370, 31%] with alpha = 1.2.
#> ● Beta C1 [n = 2784, 55%] with mean = 0.48.
#> ● Beta C2 [n = 846, 14%] with mean = 0.15.
#> ℹ Score(s): NLL = -5671.5; ICL = -10359.09 (-11266.35), H = 907.26 (0). Fit
#> converged by MM in 75 steps.