mstepE {mclust}R Documentation

M-step in the EM algorithm for a parameterized MVN mixture model.

Description

Maximization step in the EM algorithm for a parameterized MVN mixture model.

Usage

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, ...)

Arguments

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.

Value

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.
  • References

    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.

    See Also

    mstep, me, estep, mclustOptions

    Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]
    
    mstepVII(data = irisMatrix, z = unmap(irisClass))
    

    [Package mclust version 2.1-11 Index]