choose_k_GMM: Visualize BIC indicator to choose the number of clusters

In evprof: Electric Vehicle Charging Sessions Profiling and Modelling

Visualize BIC indicator to choose the number of clusters

Description

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

choose_k_GMM(california_ev_sessions, k = 1:4, start = 3)

evprof documentation built on May 29, 2024, 2:36 a.m.