92_convert: Convert Matrices in Different Format

In EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution

Convert Matrices in Different Format

Description

These utility functions are to convert matrices in different formats.

Usage

LTSigma2variance(x)
variance2LTSigma(x)
LTsigma2var(x1, p = NULL)
var2LTsigma(x1)
class2Gamma(class)
Gamma2class(Gamma)

Arguments

x	a matrix/array to be converted, the dimension could be K\times p(p+1)/2 or p\times p\times K.
x1	a vector/matrix to be converted, the length and dimension could be p(p+1)/2 and p\times p.
p	dimension of matrix.
class	id of clusters for each observation, length n.
Gamma	containing posterior probabilities if normalized, otherwise containing component densities weighted by mixing proportion, dimension n\times K.

Details

LTSigma2variance converts LTSigma format to 3D array, and variance2LTSigma is the inversion function.

LTsigma2var converts LTsigma format to a matrix, and var2LTsigma is the inversion function. Note that LTsigma is one component of LTSigma.

class2Gamma converts id to a Gamma matrix where with probability 1 for the cluster where the observation belongs to, and Gamma2class converts posterior to cluster id where largest posterior is picked for each observation.

Value

A vector/matrix/array is returned.

Author(s)

Wei-Chen Chen [e-mail] and Ranjan Maitra

References

https://www.stat.iastate.edu/people/ranjan-maitra

See Also

init.EM, emcluster.

Examples

## Not run: 
library(EMCluster, quietly = TRUE)
x <- da2$LTSigma
class <- da2$class

y <- LTSigma2variance(x)
str(y)
y <- variance2LTSigma(y)
str(y)
sum(x != y)

Gamma <- class2Gamma(class)
class.new <- Gamma2class(Gamma)
sum(class != class.new)

## End(Not run)

EMCluster documentation built on Sept. 11, 2024, 5:27 p.m.