Why lorbridge? | Dataset | Subprogram 1: Binary Logistic Regression | 1a. Continuous predictor (VM per 1 SD) | 1b. Categorical predictor (VM bins, VM4 as reference) | Subprogram 2: SONSCA | Subprogram 3: DONSCA | Multinomial logistic regression with CCMs | Key References
Package: lorbridge 0.1.0
Se-Kang Kim
lorbridge: Bridging Log-Odds Ratios and Correspondence Analysis via Closeness-of-Concordance Measures
Provides a unified analytical workflow that bridges conventional binary and multinomial logistic regression with singly-ordered (SONSCA) and doubly-ordered (DONSCA) nonsymmetric correspondence analysis. Log-odds ratios (LORs) from logistic regression are re-expressed as cosine theta estimates and closeness-of-concordance measures (CCMs) -- including Yule's Q, Yule's Y, and r_meta -- on the familiar [-1, +1] scale introduced by Kim and Grochowalski (2019) <doi:10.3758/s13428-018-1161-1>. Bootstrap confidence intervals for cosine theta are provided throughout. The package is intended to help clinical and medical researchers interpret association strength from logistic regression in an intuitive, correlation-like metric, and to connect conventional regression results with geometric correspondence analysis visualisations.
Authors:
lorbridge_0.1.0.tar.gz
lorbridge_0.1.0.zip(r-4.7)lorbridge_0.1.0.zip(r-4.6)lorbridge_0.1.0.zip(r-4.5)
lorbridge_0.1.0.tgz(r-4.6-any)lorbridge_0.1.0.tgz(r-4.5-any)
lorbridge_0.1.0.tar.gz(r-4.7-any)lorbridge_0.1.0.tar.gz(r-4.6-any)
lorbridge_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lorbridge/json (API)
| # Install 'lorbridge' in R: |
| install.packages('lorbridge', repos = c('https://sekangakim.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sekangakim/lorbridge/issues
Datasets:
- lorbridge_data - Individual-Level VM and Minority Group Dataset
- tab_IQ - IQ Contingency Table
- tab_IQ_VM - IQ x VM Contingency Table
- tab_VM - VM Contingency Table
Last updated from:3705be7bea. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 140 | ||
| source / vignettes | OK | 200 | ||
| linux-release-x86_64 | OK | 153 | ||
| macos-release-arm64 | OK | 107 | ||
| macos-oldrel-arm64 | OK | 82 | ||
| windows-devel | OK | 92 | ||
| windows-release | OK | 92 | ||
| windows-oldrel | OK | 93 | ||
| wasm-release | OK | 124 |
Exports:blr_categoricalblr_continuousccm_rowcosine_theta_2rowdonsca_cosinesdonsca_fitinertia_pctlor_ci_2x2mlr_ccmsonsca_bootstrapsonsca_ccmsonsca_coordssonsca_cosines
Dependencies:askpassbase64encbslibcachemCAvariantsclicpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsgenericsggforceggplot2ggrepelgluegridExtragtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrMASSmemoisemimennetopensslotelpillarpkgconfigplotlypolyclippromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrsyssystemfontstibbletidyrtidyselecttinytextweenrutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Binary Logistic Regression with a Categorical (Ordinal) Predictor | blr_categorical |
| Binary Logistic Regression with a Continuous Predictor | blr_continuous |
| Closeness-of-Concordance Measures from OR and CI Endpoints | ccm_row |
| Cosine Theta from a 2-Row Correspondence Analysis (SVD) | cosine_theta_2row |
| Doubly-Anchored Cosine Theta for DONSCA | donsca_cosines |
| Fit a DONSCA Model | donsca_fit |
| Percent Inertia from SONSCA | inertia_pct |
| Log-Odds Ratio with Haldane-Anscombe Correction | lor_ci_2x2 |
| Individual-Level VM and Minority Group Dataset | lorbridge_data |
| Multinomial Logistic Regression with CCMs (DONSCA Bridge) | mlr_ccm |
| Bootstrap Confidence Intervals for SONSCA Cosine Theta | sonsca_bootstrap |
| Pairwise CCMs for SONSCA | sonsca_ccm |
| SONSCA Column-Isometric Coordinates | sonsca_coords |
| Doubly-Anchored Cosine Theta for SONSCA | sonsca_cosines |
| IQ Contingency Table (4 Races x 6 IQ Bins) | tab_IQ |
| IQ x VM Contingency Table (6 IQ Bins x 6 VM Bins) | tab_IQ_VM |
| VM Contingency Table (4 Races x 6 VM Bins) | tab_VM |