Methods Implemented | Exponential Family PCA | Logistic PCA | Convex Logistic PCA | Example | Classes | Printing and Plotting | Fitted and Predicted Estimates | Large Data
Package: logisticPCA 0.2.9000
logisticPCA: Binary Dimensionality Reduction
Dimensionality reduction techniques for binary data including logistic PCA.
Authors:
logisticPCA_0.2.9000.tar.gz
logisticPCA_0.2.9000.zip(r-4.7)logisticPCA_0.2.9000.zip(r-4.6)logisticPCA_0.2.9000.zip(r-4.5)
logisticPCA_0.2.9000.tgz(r-4.6-x86_64)logisticPCA_0.2.9000.tgz(r-4.6-arm64)logisticPCA_0.2.9000.tgz(r-4.5-x86_64)logisticPCA_0.2.9000.tgz(r-4.5-arm64)
logisticPCA_0.2.9000.tar.gz(r-4.7-arm64)logisticPCA_0.2.9000.tar.gz(r-4.7-x86_64)logisticPCA_0.2.9000.tar.gz(r-4.6-arm64)logisticPCA_0.2.9000.tar.gz(r-4.6-x86_64)
logisticPCA_0.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
logisticPCA/json (API)
| # Install 'logisticPCA' in R: |
| install.packages('logisticPCA', repos = c('https://andland.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andland/logisticpca/issues
Datasets:
- house_votes84 - United States Congressional Voting Records 1984
Last updated from:26363c9695. Checks:11 WARNING, 1 ERROR, 1 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 127 | ||
| linux-devel-x86_64 | WARNING | 129 | ||
| source / vignettes | ERROR | 191 | ||
| linux-release-arm64 | WARNING | 142 | ||
| linux-release-x86_64 | WARNING | 135 | ||
| macos-release-arm64 | WARNING | 123 | ||
| macos-release-x86_64 | WARNING | 196 | ||
| macos-oldrel-arm64 | WARNING | 126 | ||
| macos-oldrel-x86_64 | WARNING | 427 | ||
| windows-devel | WARNING | 91 | ||
| windows-release | WARNING | 79 | ||
| windows-oldrel | WARNING | 85 | ||
| wasm-release | OK | 134 |
Exports:convexLogisticPCAcv.clpcacv.lpcacv.lsvdinv.logit.matlog_like_BernoullilogisticPCAlogisticSVDproject.Fantope
Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.
An Introduction to the logisticPCA R Package
Last update: 2020-09-07
Started: 2015-08-29
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| logisticPCA-package | logisticPCA-package |
| Convex Logistic Principal Component Analysis | convexLogisticPCA |
| CV for convex logistic PCA | cv.clpca |
| CV for logistic PCA | cv.lpca |
| CV for logistic SVD | cv.lsvd |
| Fitted values using logistic PCA | fitted.lpca |
| Fitted values using logistic SVD | fitted.lsvd |
| United States Congressional Voting Records 1984 | house_votes84 |
| Inverse logit for matrices | inv.logit.mat |
| Bernoulli Log Likelihood | log_like_Bernoulli |
| Logistic Principal Component Analysis | logisticPCA |
| Logistic Singular Value Decomposition | logisticSVD |
| Plot convex logistic PCA | plot.clpca |
| Plot CV for logistic PCA | plot.cv.lpca |
| Plot logistic PCA | plot.lpca |
| Plot logistic SVD | plot.lsvd |
| Predict Convex Logistic PCA scores or reconstruction on new data | predict.clpca |
| Predict Logistic PCA scores or reconstruction on new data | predict.lpca |
| Predict Logistic SVD left singular values or reconstruction on new data | predict.lsvd |
| Project onto the Fantope | project.Fantope |
