Description Usage Arguments Details Value Author(s) References Examples
Dimensionreduction transformations applied to an input data matrix. Currently on the principal component transformation and its inverse.
1 
x 
(n, d)matrix of data (typically before training or
after sampling). If 
mu 
if 
Gamma 
if 
inverse 

... 
additional arguments passed to the underlying

Conceptually, the principal component transformation transforms a
vector X to a vector Y where
Y = Gamma^T (X  mu),
where μ is the mean vector of X
and Gamma is the (d, d)matrix whose
columns contains the orthonormal eigenvectors of cov(X)
.
The corresponding (conceptual) inverse transformation is X = mu + Gamma Y.
See McNeil et al. (2015, Section 6.4.5).
If inverse = TRUE
, the transformed data whose rows contain
X = mu + Gamma Y, where
Y is one row of x
. See the details below for the
notation.
If inverse = FALSE
, a list
containing:
PCs
:(n, d)matrix of principal components.
cumvar
:cumulative variances; the jth entry provides the fraction of the explained variance of the first j principal components.
sd
:sample standard deviations of the transformed data.
lambda
:eigenvalues of cov(x)
.
mu
:dvector of centers of x
(see also
above) typically provided to PCA_trafo(, inverse = TRUE)
.
Gamma
:(d, d)matrix of principal axes (see also
above) typically provided to PCA_trafo(, inverse = TRUE)
.
Marius Hofert
McNeil, A. J., Frey, R., and Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques, Tools. Princeton University Press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Generate data
library(copula)
set.seed(271)
X < qt(rCopula(1000, gumbelCopula(2, dim = 10)), df = 3.5)
pairs(X, gap = 0, pch = ".")
## Principal component transformation
PCA < PCA_trafo(X)
Y < PCA$PCs
PCA$cumvar[3] # fraction of variance explained by the first 3 principal components
which.max(PCA$cumvar > 0.9) # number of principal components it takes to explain 90%
## Biplot (plot of the first two principal components = data transformed with
## the first two principal axes)
plot(Y[,1:2])
## Transform back and compare
X. < PCA_trafo(Y, mu = PCA$mu, Gamma = PCA$Gamma, inverse = TRUE)
stopifnot(all.equal(X., X))
## Note: One typically transforms back with only some of the principal axes
X. < PCA_trafo(Y[,1:3], mu = PCA$mu, # mu determines the dimension to transform to
Gamma = PCA$Gamma, # must be of dim. (length(mu), k) for k >= ncol(x)
inverse = TRUE)
stopifnot(dim(X.) == c(1000, 10))
## Note: We (typically) transform back to the original dimension.
pairs(X., gap = 0, pch = ".") # pairs of backtransformed first three PCs

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