Description Usage Arguments Details Value References Examples
Estimation of a mixture of Gaussian covariance or concentration graph models using structuralEM algorithm. The mixture model returned is the optimal model according to BIC.
1 2 3 4 5 6 7 8 9 10 11 12  mixGGM(data, K = 1:3,
model = c("covariance", "concentration"),
search = c("stepforw", "stepback", "ga"),
penalty = c("bic", "ebic", "erdos", "power"),
beta = NULL,
regularize = FALSE, regHyperPar = NULL,
ctrlEm = ctrlEM(),
ctrlStep = ctrlSTEP(), ctrlGa = ctrlGA(),
ctrlIcf = ctrlICF(),
keepAll = FALSE,
parallel = FALSE,
verbose = interactive())

data 
A dataframe or matrix, where rows correspond to observations and columns to variables. Categorical variables are not allowed. 
K 
An integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated. 
model 
The type of Gaussian graphical model. Default is 
search 
The type of structure search algorithm. If 
penalty 
The penalty function used to define a criterion for scoring the candidate graph configurations. Default is 
beta 
The hyperparameter of the penalty function. See "Details" and 
regularize 
A logical argument indicating if Bayesian regularization should be performed. Default to 
regHyperPar 
A list of hyper parameters for Bayesian regularization. Only used when 
ctrlEm 
A list of control parameters used in the structuralEM algorithm; see also 
ctrlStep 
A list of control parameters used in the stepwise search; see also 
ctrlGa 
A list of control parameters for the genetic algorithm; see also 
ctrlIcf 
A list of control parameters employed in the algorithm for estimation of graphical model parameters; see also 
keepAll 
A logical argument. If 
parallel 
A logical argument indicating if parallel computation should be used for structure search in the M step of the structuralEM algorithm. If TRUE, all the available cores are used. The argument could also be set to a numeric integer value specifying the number of cores to be employed. 
verbose 
If 
Estimation of a mixture of Gaussian graphical models by means of maximization of a penalized loglikelihood via structuralEM algorithm. The mixture model in output is the optimal model selected by BIC.
If model = "covariance"
, a mixture of Gaussian covariance graph models is estimated. The Gaussian mixture is parameterized in terms of the components covariance matrices and the component adjacency matrices correspond to marginal independence constraints among the variables:
X ~ sum_k tau_k N(mu_k, Sigma_k) with Sigma_k in C_G(A_k)
Variables associated to two nonconnected edges in the graphs are marginally independent and have different marginal association patterns across the mixture components. As a result, the covariance matrices sigma
are estimated to be sparse according to the inferred graph structures.
If model = "concentration"
, estimation of a mixture of Gaussian concentration graph model is performed. The Gaussian mixture is parameterized in terms of the components concentration matrices and the component adjacency matrices correspond to conditional independence constraints among the variables:
X ~ sum_k N(mu_k, Omega_k) with Omega_k in C_G(A_k)
Variables associated to two nonadjacent edges in the graph are conditionally independent given their common neighbors and have different conditional dependence patterns across the mixture components. It results in the concentration matrices omega
being estimated to be sparse according to the inferred graph structures.
Arguments penalty
and search
are used to define the type of penalty on the graph configuration and the structure search method in the structuralEM algorithm. The penalization term depends on the hyperparameter beta
according to the type of penalty function. See searchGGM
and penalty
for more details.
An object of class 'mixGGM'
containing the optimal estimated mixture of Gaussian graphical models.
The output is a list containing:
parameters 
A list with the following components:

graph 
An array with the adjacency matrices corresponding to the optimal marginal or conditional independence graphs for each mixture component. 
N 
Number of observations in the data. 
V 
Number of variables in the data, corresponding to the number of nodes in the graphs. 
K 
Number of selected mixture components. 
loglik 
Value of the maximized loglikelihood. 
loglikPen 
Value of the maximized penalized loglikelihood. 
loglikReg 
Value of the maximized regularized loglikelihood. If 
nPar 
A vector with two entries:

z 
A matrix whose 
classification 
Classification corresponding to the maximum a posteriori of matrix 
bic 
Optimal BIC value. 
BIC 
All BIC values. 
data 
The data matrix provided in input. 
model 
Estimated model type, whether 
penalty 
The type of penalty on the graph structure. 
search 
The search method used for graph structure search. 
keepAll 
A list containing all the estimated models. Provided in output only when 
Fop, M., Murphy, T.B., and Scrucca, L. (2018) Modelbased clustering with sparse covariance matrices. **Statistics and Computing**. URL: https://doi.org/10.1007/s112220189838y
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62  # fit a mixture of concentration graph models
data(iris)
mod1 < mixGGM(iris[,5], model = "concentration")
plot(mod1, what = "graph")
plot(mod1, what = "classification")
## Not run:
# a simple simulated data example
library(MASS)
N < 200
tau < c(0.3, 0.7)
Nk < rowSums( rmultinom(N, 1, tau) )
class < rep(1:2, Nk)
sigma1 < diag(2) # independent variables
sigma2 < matrix( c(1,0.9,0.9,1), 2,2 ) # correlated variables
mu1 < c(0, 0)
mu2 < c(2, 3)
x < rbind( MASS::mvrnorm(Nk[1], mu1, sigma1),
MASS::mvrnorm(Nk[2], mu2, sigma2)
)
mod2 < mixGGM(x)
plot(mod2)
plot(mod2, what = "classification")
# fit a mixture of covariance graph models
data(wine, package = "gclus")
mod3 < mixGGM(wine[,1], K = 1:4, model = "covariance",
penalty = "erdos", beta = 0.01)
plot(mod3, what = "graph")
plot(mod3, what = "classification", dimens = 1:4)
# complex simulated data example
N < 500
V < 20
tau < c(0.3, 0.7)
Nk < rowSums( rmultinom(N, 1, tau) )
class < rep(1:2, Nk)
sigma1 < rWishart(1, V+1, diag(V))[,,1]
mu1 < rep(0, V)
mu2 < rnorm(V, 0.5, 2)
x1 < MASS::mvrnorm(Nk[1], mu1, sigma1)
x2 < matrix(NA, Nk[2], V)
x2[,1] < rnorm(Nk[2])
for ( j in 2:V ) x2[,j] < x2[,j1] + rnorm(Nk[2], mu2[j], sd = 0.5)
x < rbind(x1, x2)
#
mod4 < mixGGM(x, K = 1:4, model = "concentration",
penalty = "ebic", beta = 0.5)
plot(mod4, what = "classification", dimens = c(1,5,10,15,20) )
plot(mod4, what = "graph")
plot(mod4, what = "adjacency")
table(class, mod4$classification)
#
mc < mclust::Mclust(x, G = 1:4)
mc$bic
mod4$bic
## End(Not run)

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