Performs a principal components analysis on the given data matrix projection=SammonsMapping(Data)
PCA(Data,OutputDimension=2,Scale=FALSE,Center=FALSE,PlotIt=FALSE,Cls)
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio
the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors)
the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)
matrix with PCA transformed Data
the centering used, or FALSE
the scaling used, or FALSE
numerical matrix of data: n cases in rows, d variables in columns
Number of dimensions in the Outputspace, default=2
a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale.
a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.
Michael Thrun
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (tools:::Rd_expr_doi("10.1007/978-3-658-20540-9")).
data('Hepta')
Data=Hepta$Data
Proj=PCA(Data)
if (FALSE) {
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)
}
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