get_best_model {mixture}

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 <[e-mail]>

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)