main_loop: GPCM Internal C++ Call

In mixture: Mixture Models for Clustering and Classification

GPCM Internal C++ Call

Description

This function is the internal C++ function call within the gpcm function. This is a raw C++ function call, meaning it has no checks for proper inputs so it may fail to run without giving proper errors. Please ensure all arguements are valid. main_loop is useful for writing parallizations of the gpcm function. All arguement descriptions are given in terms of their corresponding C++ types.

Usage

main_loop(X, G, model_id, 
        model_type, in_zigs, 
        in_nmax, in_l_tol, in_m_iter_max,
        in_m_tol, anneals, t_burn = 5L) 

Arguments

X	A matrix or data frame such that rows correspond to observations and columns correspond to variables. Note that this function currently only works with multivariate data p > 1.
G	A single positive integer value representing number of groups.
model_id	An integer representing the model_id, is useful for keeping track within parallizations. Not to be confused with model_type.
model_type	The type of covariance model you wish to run. Lexicon is given as follows: "0" = "EII", "1" = "VII", "2" = "EEI" , "3" = "EVI", "4" = "VEI", "5" = "VVI", "6" = "EEE", "7" = "VEE", "8" = "EVE", "9" = "EEV", "10" = "VVE", "11" = "EVV", "12" = "VEV", "13" = "VVV"
in_zigs	A n times G a posteriori matrix resembling the probability of observation i belonging to group G. Rows must sum to one, have the proper dimensions, and be positive.
in_nmax	Positive integer value resembling the maximum amount of iterations for the EM.
in_l_tol	A likelihood tolerance for convergence.
in_m_iter_max	For certain models, where applicable, the number of iterations for the maximization step.
in_m_tol	For certain models, where applicable, the tolerance for the maximization step.
anneals	A vector of doubles representing the deterministic annealing settings.
t_burn	A positive integer representing the number of burn steps if missing data (NAs) are detected.

Details

Be extremly careful running this function, it is known to crash systems without proper exception handling. Consider using the package parallel to estimate all possible models at the same time.

Value

zigs	a postereori matrix
G	An integer representing the number of groups.
sigs	A vector of covariance matrices for each group (note you may have to reshape this)
mus	A vector of mean vectors for each group

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: 

data("x2")
data_in = as.matrix(x2,ncol = 2)
n_iter = 1000

in_g = 3
n = dim(data_in)[1]
model_string <- "VVE"
in_model_type <- switch(model_string, "EII" = 0,"VII" = 1,  
              "EEI" = 2,  "EVI" = 3,  "VEI" = 4,  "VVI" = 5,  "EEE" = 6,  
              "VEE" = 7,  "EVE" = 8,  "EEV" = 9,  "VVE" = 10,
              "EVV" = 11,"VEV" = 12,"VVV" = 13)

zigs_in <- z_ig_random_soft(n,in_g)

m2 = main_loop(X = data_in, # data in
               G = 3, # number of groups
               model_id = 1, # model id for parallelization later
               model_type = in_model_type,
               in_zigs = zigs_in, # initializaiton
               in_nmax = n_iter, # number of iterations
               in_l_tol = 1e-12, # likilihood tolerance
               in_m_iter_max = 20, # maximium iterations for matrices
               in_m_tol = 1e-8,
               anneals=c(0.5,0.7,0.9,1)) 

plot(data_in,col = MAP(m2$zigs) + 1)

## End(Not run)

mixture documentation built on May 29, 2024, 1:47 a.m.