public class PCAbySVD extends Object
prcomp.| Constructor and Description |
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PCAbySVD(Matrix data)
Performs Principal Component Analysis, using the preferred SVD method, on
a centered and scaled data matrix.
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PCAbySVD(Matrix data,
boolean centered,
boolean scaled)
Performs Principal Component Analysis, using the preferred SVD method, on
a data matrix (possibly centered and/or scaled).
|
PCAbySVD(Matrix data,
Vector mean,
Vector scale)
Performs Principal Component Analysis, using the preferred SVD method, on
a data matrix with (optional) mean vector and scaling vector provided.
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| Modifier and Type | Method and Description |
|---|---|
DenseVector |
cumulativeProportionVar()
Gets the cumulative proportion of overall variance explained by the
principal components
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ImmutableMatrix |
data()
Gets the original data matrix.
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Vector |
loading(int i)
Gets the loading vector of the i-th principal component.
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Matrix |
loadings()
Gets the matrix of variable loadings.
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Vector |
mean()
Gets the sample means that were subtracted.
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int |
nFactors()
Gets the number of variables in the original data.
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int |
nObs()
Gets the number of observations in the original data; sample size.
|
Vector |
proportionVar()
Gets the proportion of overall variance explained by each of the
principal components.
|
double |
proportionVar(int i)
Gets the proportion of overall variance explained by the i-th
principal component.
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Vector |
scale()
Gets the scalings applied to each variable.
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Matrix |
scores()
Gets the scores of supplied data on the principal components.
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double |
sdPrincipalComponent(int i)
Gets the standard deviation of the i-th principal component.
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DenseVector |
sdPrincipalComponents()
Gets the standard deviations of the principal components (i.e., the
square roots of the eigenvalues of the correlation (or covariance)
matrix, though the calculation is actually done with the singular values
of the data matrix)
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SVD |
svd()
Gets the Singular Value Decomposition (SVD) of matrix X.
|
Matrix |
X()
Gets the (possibly centered and/or scaled) data matrix X used for
the PCA.
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public PCAbySVD(Matrix data, Vector mean, Vector scale)
data - a matrix that represents the original datamean - an optional mean vector (of length equal to nFactors)
to be subtracted regardless of the flag centeredscale - an optional scaling vector (of length equal to
nFactors) to be divided regardless of the flag
scaledpublic PCAbySVD(Matrix data, boolean centered, boolean scaled)
data - a matrix that represents the original datacentered - a logical value indicating whether the variables should
be shifted to be zero centeredscaled - a logical value indicating whether the variables should
be scaled to have unit variance before the analysis takes
place (N.B. in general scaling is advisable; however, it should only be
used if there is no constant variable)public PCAbySVD(Matrix data)
data - a matrix that represents the original datapublic Vector mean()
PCApublic Vector scale()
PCApublic SVD svd()
public DenseVector sdPrincipalComponents()
public Matrix loadings()
PCApublic ImmutableMatrix data()
public int nObs()
PCApublic int nFactors()
PCApublic Matrix X()
PCApublic double sdPrincipalComponent(int i)
PCAsdPrincipalComponent in interface PCAi - an index, counting from 1public Vector loading(int i)
PCApublic Vector proportionVar()
PCAproportionVar in interface PCApublic double proportionVar(int i)
PCAproportionVar in interface PCAi - an index, counting from 1public DenseVector cumulativeProportionVar()
PCAcumulativeProportionVar in interface PCACopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.