| em {mclust} | R Documentation |
Implements the EM algorithm for parameterized MVN mixture models, starting with the expectation step.
em(modelName, data, mu, ...)
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. |
mu |
The mean for each component. If there is more than one component,
mu is a matrix whose columns are the means of the
components.
|
... |
Arguments for model-specific em functions. Specifically:
|
This function can be used with an indirect or list call
using do.call, allowing the output
of e.g. mstep to be passed
without the need to specify individual parameters as arguments.
A list including the following components:
z |
A matrix whose [i,k]th entry is the conditional probability of
the ith observation belonging to the kth component of the
mixture.
|
loglik |
The logliklihood for the data in the mixture model. |
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. |
modelName |
A character string identifying the model (same as the input argument). |
Attributes: |
|
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.
emE, ...,
emVVV,
estep,
me,
mstep,
mclustOptions,
do.call
data(iris)
irisMatrix <- as.matrix(iris[,1:4])
irisClass <- iris[,5]
msEst <- mstep(modelName = "EEE", data = irisMatrix,
z = unmap(irisClass))
names(msEst)
em(modelName = msEst$modelName, data = irisMatrix,
mu = msEst$mu, Sigma = msEst$Sigma, pro = msEst$pro)
## Not run:
do.call("em", c(list(data = irisMatrix), msEst)) ## alternative call
## End(Not run)