mstepE {mclust} | R Documentation |
Maximization step in the EM algorithm for a parameterized MVN mixture model.
mstepE(data, z, equalPro, noise = FALSE, ...) mstepV(data, z, equalPro, noise = FALSE, ...) mstepEII(data, z, equalPro, noise = FALSE, ...) mstepVII(data, z, equalPro, noise = FALSE, ...) mstepEEI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...) mstepVEI(data, z, equalPro, noise = FALSE, eps, tol, itmax, warnSingular, ...) mstepEVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...) mstepVVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...) mstepEEE(data, z, equalPro, noise = FALSE, ...) mstepEEV(data, z, equalPro, noise = FALSE, eps, warnSingular, ...) mstepVVV(data, z, equalPro, noise = FALSE, ...)
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.
|
equalPro |
A logical value indicating whether or not the components in the model are
present in equal proportions.
The default is .Mclust\$equalPro .
|
noise |
A logical value indicating whether or not the model includes a Poisson noise component. The default assumes there is no noise component. |
eps |
A scalar tolerance for deciding when to terminate computations due to
computational singularity in covariances.
Smaller values of eps allows computations to
proceed nearer to singularity.
The default is .Mclust\$eps .
Not used for models "EII", "VII", "EEE", "VVV". |
tol |
For models with iterative M-step ("VEI", "VEE", "VVE", "VEV"),
a scalar tolerance for relative convergence of the parameters.
The default is .Mclust\$tol .
|
itmax |
For models with iterative M-step ("VEI", "VEE", "VVE", "VEV"),
an integer limit on the number of EM iterations.
The default is .Mclust\$itmax .
|
warnSingular |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is .Mclust\$warnSingular .
Not used for models "EII", "VII", "EEE", "VVV". |
... |
Provided to allow lists with elements other than the arguments can
be passed in indirect or list calls with do.call .
|
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.
mstep
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me
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estep
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mclustOptions
data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5] mstepVII(data = irisMatrix, z = unmap(irisClass))