choose_k_GMM {evprof} | R Documentation |
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 |
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)