Gaussian Mixture Models (GMM) — gm

gm_clust defines a model that fits clusters based on fitting a specified number of multivariate Gaussian distributions (MVG) to the data.

There are multiple implementations for this model, and the implementation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

mclust

Usage

gm_clust(
  mode = "partition",
  engine = "mclust",
  num_clusters = NULL,
  circular = TRUE,
  shared_size = TRUE,
  zero_covariance = TRUE,
  shared_orientation = TRUE,
  shared_shape = TRUE
)

Arguments

mode: A single character string for the type of model. The only possible value for this model is "partition".
engine: A single character string specifying what computational engine to use for fitting. The engine for this model is "mclust".
num_clusters: Positive integer, number of clusters in model (required).
circular: Boolean, whether or not to fit circular MVG distributions for each cluster. Default TRUE.
shared_size: Boolean, whether each cluster MVG should have the same size/volume. Default TRUE.
zero_covariance: Boolean, whether or not to assign covariances of 0 for each MVG. Default TRUE.
shared_orientation: Boolean, whether each cluster MVG should have the same orientation. Default TRUE.
shared_shape: Boolean, whether each cluster MVG should have the same shape. Default TRUE.

Value

A gm_clust cluster specification.

Details

What does it mean to predict?

To predict the cluster assignment for a new observation, we determine which cluster a point has the highest probability of belonging to.

Examples

# Show all engines
modelenv::get_from_env("gm_clust")
#> # A tibble: 1 × 2
#>   engine mode     
#>   <chr>  <chr>    
#> 1 mclust partition

gm_clust()
#> GMM Clustering Specification (partition)
#> 
#> Main Arguments:
#>   circular = TRUE
#>   zero_covariance = TRUE
#>   shared_orientation = TRUE
#>   shared_shape = TRUE
#>   shared_size = TRUE
#> 
#> Computational engine: mclust 
#>