mstep {mclust} | R Documentation |
Maximization step in the EM algorithm for parameterized MVN mixture models.
mstep(modelName, data, z, ...)
modelName |
A character string indicating the model: "E": equal variance (one-dimensional) "V": variable variance (one-dimensional) "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 "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation |
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
z |
A matrix whose [i,k] th entry is the
conditional probability of the ith observation belonging to
the kth component of the mixture.
|
... |
Any number of the following:
|
A list including the following components:
mu |
A matrix whose kth column is the mean of the kth component of the mixture model. |
sigma |
For multidimensional models, a three dimensional array
in which the [,,k] th entry gives the
the covariance for the kth group in the best model. <br>
For one-dimensional models, either a scalar giving a common variance for
the groups or a vector whose entries are the variances for each group
in the best model.
|
pro |
A vector whose kth component is the mixing proportion for the kth component of the mixture model. |
z |
A matrix whose [i,k] th entry is the
conditional probability of the ith observation belonging to
the kth component of the mixture.
|
modelName |
A character string identifying the model (same as the input argument). |
Attributes: |
"info" : Information on the iteration.
"warn" : An appropriate warning if problems are
encountered in the computations.
|
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
mstepE
, ...,
mstepVVV
,
me
,
estep
,
mclustOptions
.
data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5] mstep(modelName = "VII", data = irisMatrix, z = unmap(irisClass))