Package: hddplot 0.59-1
hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots
Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that are intended for didactic use. The package implements, and extends, methods described in J.H. Maindonald and C.J. Burden (2005) <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
Authors:
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hddplot.pdf |hddplot.html✨
hddplot/json (API)
| # Install 'hddplot' in R: |
| install.packages('hddplot', repos = c('https://jhmaindonald.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jhmaindonald/hddplot/issues
Last updated 2 years agofrom:d42bcfb84c. Checks:6 OK, 3 ERROR. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 31 2025 |
| R-4.5-win | ERROR | Mar 31 2025 |
| R-4.5-mac | ERROR | Mar 31 2025 |
| R-4.5-linux | ERROR | Mar 31 2025 |
| R-4.4-win | OK | Mar 31 2025 |
| R-4.4-mac | OK | Mar 31 2025 |
| R-4.4-linux | OK | Mar 31 2025 |
| R-4.3-win | OK | Mar 31 2025 |
| R-4.3-mac | OK | Mar 31 2025 |
Exports:accTrainTestaovFbyrowcvdisccvscoresdefectiveCVdiscdivideUporderFeaturespcpplotTrainTestqqthinscoreplotsimulateScores
Dependencies:BiobaseBiocGenericsgenericslatticeMASSMatrixmulttestsurvival
