Package: optiscale 1.2.3
Dave Armstrong
optiscale: Optimal Scaling
Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. The targets are usually predicted values from a statistical model. If the data are nominal level, then the transformation must be identity-preserving. If the data are ordinal level, then the transformation must be monotonic. If the data are discrete, then tied data values must remain tied in the optimal transformation. If the data are continuous, then tied data values can be untied in the optimal transformation.
Authors:
optiscale_1.2.3.tar.gz
optiscale_1.2.3.tar.gz(r-4.5-noble)optiscale_1.2.3.tar.gz(r-4.4-noble)
optiscale_1.2.3.tgz(r-4.4-emscripten)optiscale_1.2.3.tgz(r-4.3-emscripten)
optiscale.pdf |optiscale.html✨
optiscale/json (API)
| # Install 'optiscale' in R: |
| install.packages('optiscale', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- elec92 - Public Opinion During the 1992 U.S. Presidential Election
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 months agofrom:8e1a9cd0d3. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 14 2024 |
| R-4.5-linux | OK | Oct 14 2024 |
Exports:calc.stressopscaleos.plotshep.plotshepardstress
Dependencies:lattice
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Optimal Scaling of a Data Vector | optiscale-package optiscale |
| Public Opinion During the 1992 U.S. Presidential Election | elec92 |
| S3 methods for opscale | Methods plot.opscale print.opscale print.summary.opscale summary.opscale |
| Optimal scaling of a data vector | opscale |
| Graph of optimal scaling transformation | os.plot |
| Shepard diagram for opscale | shep.plot shepard |
| Stress coefficients for 'opscale' | calc.stress stress |