get_best_model: Best Model Extractor
In mixture: Mixture Models for Clustering and Classification

get_best_model

R Documentation

Best Model Extractor

Description

Carries out model-based clustering or classification using some or all of the 14 parsimonious Gaussian clustering models (GPCM).

Usage

get_best_model(gpcm_model)

Arguments

gpcm_model

An input of class gpcm.

Details

Extracts the best model based on BIC.

Value

An object of class gpcm_best is a list with components:

`model_type`	A string containg summarized information about the type of model estimated (Covariance structure and number of groups).
`model_obj`	An internal list containing all parameters returned from the C++ call.
`BIC`	Bayesian Index Criterion (positive scale, bigger is better).
`loglik`	Log liklihood from the estimated model.
`nparam`	Number of a parameters in the mode.
`startobject`	The type of object inputted into `start`.
`G`	An integer representing the number of groups.
`cov_type`	A string representing the type of covariance matrix (see 14 models).
`status`	Convergence status of EM algorithm according to Aitken's Acceleration
`map`	A vector of integers indicating the maximum a posteriori classifications for the best model.
`row_tags`	If there were NAs in the original dataset, a vector of indices referencing the row of the imputed vectors is given.

Author(s)

Nik Pocuca, Ryan P. Browne and Paul D. McNicholas.

Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>

References

Browne, R.P. and McNicholas, P.D. (2014). Estimating common principal components in high dimensions. Advances in Data Analysis and Classification 8(2), 217-226.

Zhou, H. and Lange, K. (2010). On the bumpy road to the dominant mode. Scandinavian Journal of Statistics 37, 612-631.

Celeux, G., Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition 28(5), 781-793.

Examples

## Not run: 

# load dataset and perform model search. 
data(x2)
data_in <- matrix(x2,ncol = 2)
mm <- mixture::gpcm(data = data_in,G = 1:7,
           start = 0,
           veo = FALSE,pprogress=FALSE)

# get best model 
best = get_best_model(mm)
best

## End(Not run)

mixture documentation built on Sept. 27, 2023, 1:06 a.m.