Runs the EM algorithm for matrix clustering
MatTrans.EM(Y, initial = NULL, la = NULL, nu = NULL,
model = NULL, trans = "None", la.type = 0,
row.skew = TRUE, col.skew = TRUE, tol = 1e-05,
short.iter = NULL, long.iter = 1000, all.models = TRUE,
size.control = 0, silent = TRUE)
scale parameter set by the user
parsimonious models
model types
log likelihood values
bic values
best parsimonious model
best model type
best logliklihood
best bic
transformation type
dataset of random matrices (p x T x n), n random matrices of dimensionality (p x T)
initialization parameters provided by function MatTrans.init()
initial skewness for rows (K x p)
initial skewness for columns (K x T)
parsimonious model type, if null, then all 210 models are run
transformation method: None (Gaussian models), Power, Manly
lambda type 0 or 1, 0: unrestricted, 1: same lambda across all variables
if skewness for rows are fitted: TRUE or FALSE
if skewness for columns are fitted: TRUE or FALSE
tolerance level
number of short EM iterations; if not specified, just run long EM
number of long EM iterations
if true, run long EM for all models; otherwise just the best model returned by short EM in terms of BIC
minimum size of clusters allowed for controlling spurious solutions
whether to produce output of steps or not
Runs the EM algorithm for modeling and clustering matrices for a provided dataset. Both matrix Gaussian mixture, matrix Power mixture and matrix Manly transformation mixture can be employed. The user should use the MatTrans.init() function to get initial parameters and input them as 'initial'. In the case when transformation parameters are not provided but 'trans' is specified to be 'Power' or 'Manly', 'la' and 'nu' take value of 0.5. 'model' can be specified as 'X-XXX-XX'. The first digit 'X' stands for the mean structure. It is either 'G': general mean or 'A': additive mean. The second 'XXX' specifies the variance-covariance Sigma. There are 14 options including EII, VII, EEI, VEI, EVI, VVI, EEE, EVE, VEE, VVE, EEV, VEV, EVV and VVV with detailed explanation as follows: "EII" spherical, equal volume "VII" spherical, unequal volume "EEI" diagonal, equal volume and shape "VEI" diagonal, varying volume, equal shape "EVI" diagonal, equal volume, varying shape "VVI" diagonal, varying volume and shape "EEE" ellipsoidal, equal volume, shape, and orientation "EVE" ellipsoidal, equal volume and orientation (*) "VEE" ellipsoidal, equal shape and orientation (*) "VVE" ellipsoidal, equal orientation (*) "EEV" ellipsoidal, equal volume and equal shape "VEV" ellipsoidal, equal shape "EVV" ellipsoidal, equal volume (*) "VVV" ellipsoidal, varying volume, shape, and orientation The last 2-digit 'XX' specifies the variance-covariance Psi. There are 8 options including II, EI, VI, EE, VE, EV, VV, AR. The user can specify the 'model' to be for example 'X-VVV-EV', then both 'G' and 'A' mean structures will be fitted while Sigma and Psi are fixed at 'VVV' and 'EV', respectively. Similarly, 'model' can be specified as 'G-XXX-EV' or 'G-VVV-XX' for selection of Sigma and Psi structures.