Package: pcdpca 0.4

Lukasz Kidzinski

pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series

Method extends multivariate and functional dynamic principal components to periodically correlated multivariate time series. This package allows you to compute true dynamic principal components in the presence of periodicity. We follow implementation guidelines as described in Kidzinski, Kokoszka and Jouzdani (2017), in Principal component analysis of periodically correlated functional time series <arxiv:1612.00040>.

Authors:Lukasz Kidzinski [aut, cre], Neda Jouzdani [aut], Piotr Kokoszka [aut]

pcdpca_0.4.tar.gz
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pcdpca_0.4.tgz(r-4.4-emscripten)pcdpca_0.4.tgz(r-4.3-emscripten)
pcdpca.pdf |pcdpca.html✨
pcdpca/json (API)

# Install 'pcdpca' in R:
install.packages('pcdpca', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 176 downloads 3 exports 50 dependencies

Last updated 8 years agofrom:994e422255. Checks:1 OK, 2 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-linuxNOTEMar 17 2025
R-4.4-linuxNOTEMar 17 2025

Exports:pcdpcapcdpca.inversepcdpca.scores

Dependencies:ashbitopscliclustercolorspacedeSolvefansifarverfdafdsFNNfreqdomggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmatrixcalcmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangscalestibbleutf8vctrsviridisLitewithr

Citation

To cite package ‘pcdpca’ in publications use:

Kidzinski L, Jouzdani N, Kokoszka P (2017). pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series. R package version 0.4, https://CRAN.R-project.org/package=pcdpca.

Corresponding BibTeX entry: 

  @Manual{,
    title = {pcdpca: Dynamic Principal Components for Periodically
      Correlated Functional Time Series},
    author = {Lukasz Kidzinski and Neda Jouzdani and Piotr Kokoszka},
    year = {2017},
    note = {R package version 0.4},
    url = {https://CRAN.R-project.org/package=pcdpca},
  }

Readme and manuals

pcdpca

Implementation of "Dynamic principal components of periodically correlated functional time series".

Two examples in demo directory:

  • pm10 data from Graz (comparizon with DFPCA paper)
  • simplation with parametrized periodicity

Installation

library("devtools")
install_github("kidzik/pcdpca")

Running a demo

library("pcdpca")
demo("simulation")
demo("pcdpca.pm10")

Usage

Let X be a multivariate time series, a matrix with n observations and d covariates, periodic with period = 2. Then

FF = pcdpca(X, period=2)  # finds the optimal filter
Yhat = pcdpca.scores(X, FF)  # applies the filter
Yhat[,-1] = 0 # forces the use of only one component
Xhat = pcdpca.inverse(Yhat, FF)  # deconvolution
cat(sum((X-Xhat)^2) / sum(X^2)) # variance explained