cmb.search {cmbClust}

Optimal conditioning order search

Description

Runs forward, backward, or stepwise variable selection procedure for obtaining the parsimonious conditional mixture models when all conditional orders are considered. Alternatively, runs the optimal order search algorithm, and parsimonious conditional mixture modeling for the obtained order.

Usage

cmb.search(x, l, K, method = "stepwise", all.perms  = TRUE, id0 = NULL,  n.em = 200,
em.iter = 5, EM.iter = 200, nk.min = NULL, max.spur = 5, tol = 1e-06, silent = FALSE, 
Parallel = TRUE, n.cores = 4)

Arguments

x

dataset matrix (n x p)

l

order of polynomial regression model

K

number of clusters

method

variable selection method (options 'stepwise', 'forward', 'backward' and 'none')

all.perms

conditioning order search algorithm (TRUE: full search; FALSE proposed search algorithm)

id0

initial group membership (length n)

n.em

number of short EM in emEM procedure

em.iter

maximum number of short EM iterations in emEM

EM.iter

maximum number of EM iterations

nk.min

spurious output control

max.spur

number of trials

tol

tolerance level

silent

output control

Parallel

Parallel computing

n.cores

number of cores in parallel computing

Details

Functions 'cmb.search' and 'cmb.em' have common arguments except 'all.perm'. With all.perms = TRUE, a full search is applied to data, that is running parsimonious conditional mixture modeling for all orders and recognizing the optimal order based on the BIC. Then two lists are returned: best.model stores the results for the conditional mixture model with the optimal order, and models has results for all orders. With the option all.perms = FALSE, the optimal conditional order search algorithm is applied, and then only the list best.model is returned.

Value

The list models is returned when all.perms = TRUE.

best.model

membership assignments and estimated parameters of mixture model with the optimal contioning order.

data

the input dataset

model

estimated regression models for each cluster (K x p)

id

vector of estimated membership (length n)

loglik

estimated log likelihood

BIC

Bayesian Information Criterion

Pi

vector of estimated mixing proportions (length K)

tau

matrix of estimated posterior probabilities (n x K)

beta

matrix of estimated regression parameters (K x (p + p(p-1)l/2) )

s2

matrix of estimated variances (K x p)

order

applied conditioning order length p

n_pars

number of parameters

models

membership assignments and model parameters of mixture models with all conditioning orders.

model

list of estimated regression models for all clusters (K x p x p!)

id

p! vectors of estimated memberships (n x p!)

loglik

estimated log likelihood values (length p!)

BIC

Bayesian Information Criterion values (length p!)

Pi

p! vectors of estimated mixing proportions (K x p!)

tau

p! matrices of estimated posterior probabilities (K x p!)

beta

p! matrices of estimated regression parameters (K x (p + p(p-1)l/2) x p! )

s2

p! matrices of estimated variances (K x p x p!)

order

appled conditioning orders (p! x p )

n_pars

number of parameters in p! models (length p!)

See Also

cmb.em

Examples

set.seed(1)
K = 3
l <- 2
x <- as.matrix(iris[,-5])


obj <- cmb.search(x = x, l, K, method = "stepwise", all.perms = FALSE,
Parallel = FALSE, silent = FALSE)
obj$best.model$BIC