determineOptimalModel: Determine Optimal GMM
In audrina/pcclust: PCA Analysis and Visualization for Clustering
Description
Usage
Arguments
Value
See Also
Examples
Description
Use log likelihood criterion to select optimal model for best PC set.
Usage
1
determineOptimalModel(bestPCSet)
Arguments
bestPCSet
a matrix whose columns contain the optimal principal components.
Value
summary of output from mclust::mclustBIC(<bestPCSet>) computed with optimal model parameters.
See Also
Examples
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data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
bestPCSet <- iterationResults[[length(iterationResults)]]
clusterResults <- evaluateClusterQuality(bestPCSet)
optimalModel <- determineOptimalModel(bestPCSet)
audrina/pcclust documentation built on May 31, 2019, 12:44 a.m.
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Description Usage Arguments Value See Also Examples
Description
Use log likelihood criterion to select optimal model for best PC set.
Usage
1 | determineOptimalModel(bestPCSet)
|
Arguments
bestPCSet |
a matrix whose columns contain the optimal principal components. |
Value
summary of output from mclust::mclustBIC(<bestPCSet>) computed with optimal model parameters.
See Also
Examples
1 2 3 4 5 6 7 | data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
bestPCSet <- iterationResults[[length(iterationResults)]]
clusterResults <- evaluateClusterQuality(bestPCSet)
optimalModel <- determineOptimalModel(bestPCSet)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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