In MagmaClust, as for any clustering method, the number K of clusters has to be provided as an hypothesis of the model. This function implements a model selection procedure, by maximising a variational BIC criterion, computed for different values of K. A heuristic for a fast approximation of the procedure is proposed as well, although the corresponding models would not be properly trained.
select_nb_cluster(
data,
fast_approx = TRUE,
grid_nb_cluster = 1:10,
ini_hp_k = NULL,
ini_hp_i = NULL,
kern_k = "SE",
kern_i = "SE",
plot = TRUE,
...
)A list, containing the results of model selection procedure for selecting the optimal number of clusters thanks to a V-BIC criterion maximisation. The elements of the list are:
best_k: An integer, indicating the resulting optimal number of clusters
seq_vbic: A vector, corresponding to the sequence of the V-BIC values
associated with the models trained for each provided cluster's number in
grid_nb_cluster.
trained_models: A list, named by associated number of clusters, of
Magma or MagmaClust models that have been trained (or approximated if
fast_approx = T) during the model selection procedure.
A tibble or data frame. Columns required: ID, Input
, Output. Additional columns for covariates can be specified.
The ID column contains the unique names/codes used to identify each
individual/task (or batch of data).
The Input column should define the variable that is used as
reference for the observations (e.g. time for longitudinal data). The
Output column specifies the observed values (the response
variable). The data frame can also provide as many covariates as desired,
with no constraints on the column names. These covariates are additional
inputs (explanatory variables) of the models that are also observed at
each reference Input.
A boolean, indicating whether a fast approximation should
be used for selecting the number of clusters. If TRUE, each Magma or
MagmaClust model will perform only one E-step of the training, using
the same fixed values for the hyper-parameters (ini_hp_k and
ini_hp_i, or random values if not provided) in all models. The
resulting models should not be considered as trained, but this approach
provides an convenient heuristic to avoid a cumbersome model selection
procedure.
A vector of integer, corresponding to grid of values that will be tested for the number of clusters.
A tibble or data frame of hyper-parameters associated with
kern_k. The hp function can be used to draw
custom hyper-parameters with the correct format.
A tibble or data frame of hyper-parameters associated with
kern_i. The hp function can be used to draw
custom hyper-parameters with the correct format.db
A kernel function associated to the mean processes.
A kernel function associated to the individuals/tasks.
A boolean indicating whether the plot of V-BIC values for all numbers of clusters should displayed.
Any additional argument that could be passed to
train_magmaclust.