mstep {mclust} | R Documentation |
Maximization step in the EM algorithm for parameterized Gaussian mixture models.
mstep(modelName, data, z, prior = NULL, warn = NULL, ...)
modelName |
A character string indicating the model. The help file for
mclustModelNames describes the available models.
|
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.
In analyses involving noise, this should not include the
conditional probabilities for the noise component.
|
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set in .Mclust\$warn .
|
... |
Catches unused arguments in indirect or list calls via do.call .
|
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
Attributes: |
|
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
This function computes the M-step only for MVN mixtures, so in
analyses involving noise, the conditional probabilities input should
exclude those for the noise component.
In contrast to me
for the EM algorithm, computations in mstep
are carried out unless failure due to overflow would occur. To impose
stricter tolerances on a single mstep
, use me
with the
itmax component of the control
argument set to 1.
mstepE
, ...,
mstepVVV
,
emControl
,
me
,
estep
,
mclustOptions
.
mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))