Package: SpatPCA 1.3.5
Wen-Ting Wang
SpatPCA: Regularized Principal Component Analysis for Spatial Data
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <doi:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.
Authors:
SpatPCA_1.3.5.tar.gz
SpatPCA_1.3.5.tar.gz(r-4.5-noble)SpatPCA_1.3.5.tar.gz(r-4.4-noble)
SpatPCA_1.3.5.tgz(r-4.4-emscripten)SpatPCA_1.3.5.tgz(r-4.3-emscripten)
SpatPCA.pdf |SpatPCA.html✨
SpatPCA/json (API)
NEWS
| # Install 'SpatPCA' in R: |
| install.packages('SpatPCA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/egpivo/spatpca/issues
Last updated 12 months agofrom:c1d1d356f4. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 08 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 08 2024 |
Exports:checkInputDatacheckNewLocationsForSpatpcaObjectdetrendeigenFunctionfetchUpperBoundNumberEigenfunctionsplot.spatpcapredictpredictEigenfunctionscaleLocationsetCoressetGammasetL2setNumberEigenfunctionssetTau1setTau2spatialPredictionspatpcaspatpcaCVspatpcaCVWithSelectedKthinPlateSplineMatrix
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr
Capture the Dominant Spatial Pattern with One-Dimensional Locations
Rendered fromdemo-one-dim-location.Rmdusingknitr::rmarkdownon Oct 08 2024.Last update: 2023-11-13
Started: 2021-01-31
Capture the Dominant Spatial Pattern with Two-Dimensional Locations
Rendered fromdemo-two-dim-location.Rmdusingknitr::rmarkdownon Oct 08 2024.Last update: 2023-11-13
Started: 2021-01-31
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Regularized Principal Component Analysis for Spatial Data | SpatPCA-package |
| Display the cross-validation results | plot.spatpca |
| Spatial predictions on new locations | predict |
| Spatial dominant patterns on new locations | predictEigenfunction |
| Regularized PCA for spatial data | spatpca |
| Thin-plane spline matrix | thinPlateSplineMatrix |
