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Visualize BIC indicator to choose the number of clusters

Source: R/clustering.R
choose_k_GMM.Rd

The Baysian Information Criterion (BIC) is the value of the maximized loglikelihood with a penalty on the number of parameters in the model, and allows comparison of models with differing parameterizations and/or differing numbers of clusters. In general the larger the value of the BIC, the stronger the evidence for the model and number of clusters (see, e.g. Fraley and Raftery 2002a).

Usage

choose_k_GMM(
  sessions,
  k,
  mclust_tol = 1e-08,
  mclust_itmax = 10000,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions

tibble, sessions data set in evprof standard format.

k

sequence with the number of clusters, for example 1:10, for 1 to 10 clusters.

mclust_tol

tolerance parameter for clustering

mclust_itmax

maximum number of iterations

log

logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).

start

integer, start hour in the x axis of the plot.

Value

BIC plot

Examples

# \donttest{
choose_k_GMM(california_ev_sessions, k = 1:4, start = 3)

# }