mclust (version 5.2.2)

me.weighted: EM algorithm with weights starting with M-step for parameterized MVN mixture models

Description

Implements the EM algorithm for fitting MVN mixture models parameterized by eigenvalue decomposition, when observations have weights, starting with the maximization step.

Usage

me.weighted(modelName, data, z, weights = NULL, prior = NULL,  control = emControl(), Vinv = NULL, warn = NULL, ...)

Arguments

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 an initial estimate of the conditional probability of the ith observation belonging to the kth component of the mixture.

weights

A vector of positive weights, where the [i]th entry is the weight for the ith observation. If any of the weights are greater than one, then they are scaled so that the maximum weight is one.

prior

Specification of a conjugate prior on the means and variances. See the help file for priorControl for further information. The default assumes no prior.

control

A list of control parameters for EM. The defaults are set by the call emControl.

Vinv

If the model is to include a noise term, Vinv is an estimate of the reciprocal hypervolume of the data region. If set to a negative value or 0, the model will include a noise term with the reciprocal hypervolume estimated by the function hypvol. The default is not to assume a noise term in the model through the setting Vinv=NULL.

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 by warn using mclust.options.

...

Catches unused arguments in indirect or list calls via do.call.

Value

A list including the following components:

References

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 (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington. C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181. C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

me, meE,..., meVVV, em, mstep, estep, priorControl, mclustModelNames, mclustVariance, mclust.options

Examples

w <- rep(1,150)
w[1] <- 0
me.weighted(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]),weights=w)