executePCFiltering: Filter principal component data
In audrina/pcclust: PCA Analysis and Visualization for Clustering
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
Filter raw principal component data automatically until only the top 2 PCs remain.
Usage
1
executePCFiltering(pcData)
Arguments
pcData
a numerical R matrix or data frame.
Details
Indirect recursion in the case of optimal subset ambiguity (i.e. different models for maxBIC and maxLikelihood)
Value
list of iteration results from PCA filtering, where each iteation has one less PC. List elements are PCA matrices. Last entry in the list corresponds to the top 2 PCs for clustering.
See Also
Examples
1
2
3
4
data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
audrina/pcclust documentation built on May 31, 2019, 12:44 a.m.
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Description Usage Arguments Details Value See Also Examples
Description
Filter raw principal component data automatically until only the top 2 PCs remain.
Usage
1 | executePCFiltering(pcData)
|
Arguments
pcData |
a numerical R matrix or data frame. |
Details
Indirect recursion in the case of optimal subset ambiguity (i.e. different models for maxBIC and maxLikelihood)
Value
list of iteration results from PCA filtering, where each iteation has one less PC. List elements are PCA matrices. Last entry in the list corresponds to the top 2 PCs for clustering.
See Also
Examples
1 2 3 4 | data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
|
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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