Type:	Package
Title:	Bridging Log-Odds Ratios and Correspondence Analysis via Closeness-of-Concordance Measures
Version:	0.1.0
Date:	2026-04-19
Description:	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.
License:	GPL (≥ 3)
URL:	https://github.com/sekangakim/lorbridge
BugReports:	https://github.com/sekangakim/lorbridge/issues
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Imports:	CAvariants (≥ 5.5), nnet (≥ 7.3), stats
Suggests:	dplyr (≥ 1.1.0), ggplot2 (≥ 3.4.0), ggrepel (≥ 0.9.0), graphics, tidyr (≥ 1.3.0), knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-04-21 20:46:34 UTC; sekangkim
Author:	Se-Kang Kim [aut, cre]
Maintainer:	Se-Kang Kim <se-kang.kim@bcm.edu>
Repository:	CRAN
Date/Publication:	2026-04-21 21:22:21 UTC

lorbridge: Bridging Log-Odds Ratios and Correspondence Analysis

Description

lorbridge provides a unified analytical workflow that connects 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) – Yule's Q, Yule's Y, and r_meta – all on the familiar [-1, +1] scale. Bootstrap confidence intervals for cosine theta are provided throughout.

Main functions

lor_ci_2x2()

Haldane-Anscombe-corrected 2x2 LOR with Wald CI.

ccm_row()

Full CCM row (OR, LOR, YuleQ, YuleY, r_meta) with CIs.

blr_continuous()

Binary logistic regression, continuous predictor.

blr_categorical()

Binary logistic regression, categorical predictor.

cosine_theta_2row()

Cosine theta from 2-row CA via SVD.

sonsca_coords()

SONSCA column-isometric coordinates.

sonsca_cosines()

Doubly-anchored cosine theta matrix (SONSCA).

sonsca_bootstrap()

Bootstrap CIs for SONSCA cosine theta.

sonsca_ccm()

Pairwise CCMs for SONSCA contrasts.

inertia_pct()

Percent inertia per dimension.

donsca_fit()

Fit a DONSCA model.

donsca_cosines()

Doubly-anchored cosine theta (DONSCA).

mlr_ccm()

Multinomial logistic regression with CCMs.

Data

lorbridge_data

Individual-level dataset (N = 900) with VM scores, VM bins, and binary minority/majority group membership.

tab_IQ

4 x 6 contingency table: 4 racial groups x 6 IQ bins.

tab_VM

4 x 6 contingency table: 4 racial groups x 6 VM bins.

tab_IQ_VM

6 x 6 contingency table: 6 IQ bins x 6 VM bins (for DONSCA).

Author(s)

Maintainer: Se-Kang Kim se-kang.kim@bcm.edu (ORCID)

References

Kim, S.-K., & Grochowalski, J. H. (2019). Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behavior Research Methods, 51(2), 589-601. doi:10.3758/s13428-018-1161-1

Kim, S.-K. (2024). Factorization of person response profiles to identify summative profiles carrying central response patterns. Psychological Methods, 29(4), 723-730. doi:10.1037/met0000568

See Also

Useful links:

• https://github.com/sekangakim/lorbridge

• Report bugs at https://github.com/sekangakim/lorbridge/issues

Binary Logistic Regression with a Categorical (Ordinal) Predictor

Description

Fits a binary logistic regression model with a categorical predictor (treated as an unordered factor with a user-specified reference level). For each non-reference category, reports the pairwise LOR, OR, Wald confidence interval, p-value, CCMs, and cosine theta from a 2 x J simple correspondence analysis (Kim & Grochowalski, 2019 bridge).

Usage

blr_categorical(outcome, predictor, ref_level = NULL, alpha = 0.05)

Arguments

Details

Cosine theta is computed via a 1D singular value decomposition (SVD) of the standardised residual matrix of the 2 x J correspondence table, bypassing CAvariants (which requires at least 2 dimensions). In a 2-row table the 1D CA solution is exact, and cosine theta equals +1 or -1 for each non-reference column, with the sign indicating the direction of association relative to the reference category and majority group.

Value

A named list with elements:

model

The fitted glm object.

results

A data.frame with one row per non-reference category, containing LOR, OR, 95 percent CI, p-value, CCMs, and cosine theta.

tab_2xJ

The 2 x J contingency table used for cosine theta.

References

Kim, S.-K., & Grochowalski, J. H. (2019). Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behavior Research Methods, 51(2), 589-601. doi:10.3758/s13428-018-1161-1

Examples

data(lorbridge_data)
res <- blr_categorical(outcome   = lorbridge_data$minority,
                       predictor = lorbridge_data$VMbin,
                       ref_level = "VM4")
print(res$results)

Binary Logistic Regression with a Continuous Predictor

Description

Fits a binary logistic regression model with a single continuous predictor, standardised to unit standard deviation (per 1 SD). Reports the log-odds ratio (LOR), odds ratio (OR), Wald confidence interval, p-value, Nagelkerke R-squared, and the full set of closeness-of-concordance measures (CCMs) on the [-1, +1] scale.

Usage

blr_continuous(outcome, predictor, alpha = 0.05)

Arguments

Details

The predictor is standardised as (x - mean(x)) / sd(x), so the reported OR and LOR correspond to a one-standard-deviation increase. Nagelkerke R-squared is computed as: (1 - exp((2/n)(LL_null - LL_fit))) / (1 - exp((2/n) * LL_null)).

Value

A named list with elements:

model

The fitted glm object.

summary_table

A data.frame with LOR, OR, 95\ Nagelkerke R-squared, and CCMs (YuleQ, YuleY, r_meta) with CIs.

predictor_mean

Mean of the predictor used for standardisation.

predictor_sd

SD of the predictor used for standardisation.

References

Kim, S.-K., & Grochowalski, J. H. (2019). Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behavior Research Methods, 51(2), 589-601. doi:10.3758/s13428-018-1161-1

Examples

data(lorbridge_data)
res <- blr_continuous(outcome  = lorbridge_data$minority,
                      predictor = lorbridge_data$VM)
print(res$summary_table)

Closeness-of-Concordance Measures from OR and CI Endpoints

Description

Computes a full row of closeness-of-concordance measures (CCMs) from an odds ratio (OR), its confidence interval endpoints, and the corresponding log-odds ratio (LOR). CCMs include Yule's Q, Yule's Y, and the meta-analytic correlation r_meta (probit transformation of LOR), all on the [-1, +1] scale introduced by Kim and Grochowalski (2019).

Usage

ccm_row(OR, OR_lo, OR_hi, LOR, LOR_lo, LOR_hi)

Arguments

Details

Yule's Q = (OR - 1) / (OR + 1). Ranges from -1 to +1; equals the Pearson correlation for 2x2 tables under a tetrachoric model.

Yule's Y = (sqrt(OR) - 1) / (sqrt(OR) + 1). A shrunken version of Q with better sampling properties for sparse tables.

r_meta converts LOR to Cohen's d via d = LOR * sqrt(3) / pi, then to a correlation-like metric via d / sqrt(d^2 + 4). Equivalent to the biserial correlation used in meta-analysis.

Value

A one-row data.frame with columns: OR, OR_lo, OR_hi, LOR, LOR_lo, LOR_hi, YuleQ, Q_lo, Q_hi, YuleY, Y_lo, Y_hi, r_meta, r_lo, r_hi.

References

Kim, S.-K., & Grochowalski, J. H. (2019). Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behavior Research Methods, 51(2), 589-601. doi:10.3758/s13428-018-1161-1

Examples

lc <- lor_ci_2x2(30, 25, 28, 24)
ccm_row(exp(lc$lor), exp(lc$lo), exp(lc$hi), lc$lor, lc$lo, lc$hi)

Cosine Theta from a 2-Row Correspondence Analysis (SVD)

Description

Computes anchored cosine theta values from a 2 x J contingency table via a direct 1D SVD of the standardised residual matrix, bypassing the CAvariants function (which requires at least 2 dimensions). This implements the Kim and Grochowalski (2019) log-odds ratio to cosine theta bridge for 2-row tables.

Usage

cosine_theta_2row(tab_2xJ, ref_col)

Arguments

Value

A named numeric vector of cosine theta values, one per non-reference column. Values are +1 or -1 in the 1D case (sign carries the direction).

References

Kim, S.-K., & Grochowalski, J. H. (2019). Gaining from discretization of continuous data: The correspondence analysis biplot approach. Behavior Research Methods, 51(2), 589-601. doi:10.3758/s13428-018-1161-1

Examples

data(lorbridge_data)
tab <- table(lorbridge_data$minority, lorbridge_data$VMbin)
rownames(tab) <- c("Majority", "Minority")
cosine_theta_2row(tab, ref_col = "VM4")

Doubly-Anchored Cosine Theta for DONSCA

Description

Computes doubly-anchored cosine theta values for all non-anchor row and column contrasts in a DONSCA solution. Each cosine theta quantifies the geometric alignment between the direction (row_i - row_anchor) and (col_j - col_anchor) in the full multivariate CA space.

Usage

donsca_cosines(fit, col_anchor_idx, row_anchor_idx, dims = "all")

Arguments

Value

A data.frame with columns: Row, Col, cos_theta.

Fit a DONSCA Model

Description

Fits a Doubly-Ordered Nonsymmetric Correspondence Analysis (DONSCA) model via CAvariants, using all available dimensions.

Usage

donsca_fit(tab)

Arguments

Value

A CAvariants fit object containing, among others, Rprinccoord (row principal coordinates) and Cstdcoord (column standard coordinates).

Percent Inertia from SONSCA

Description

Computes the percentage of total inertia explained by each dimension in a SONSCA solution, using a direct SVD of the standardised residual matrix.

Usage

inertia_pct(tab)

Arguments

Value

Numeric vector of percent inertia values (summing to 100).

Log-Odds Ratio with Haldane-Anscombe Correction

Description

Computes the log-odds ratio (LOR) and its Wald confidence interval for a 2x2 contingency table, applying the Haldane-Anscombe continuity correction (adding 0.5 to all cells) when any cell count is zero.

Usage

lor_ci_2x2(a, b, c, d, alpha = 0.05, cc = 0.5)

Arguments

Value

A named list with elements:

lor

Point estimate of the log-odds ratio.

se

Standard error of the log-odds ratio.

lo

Lower bound of the Wald confidence interval.

hi

Upper bound of the Wald confidence interval.

References

Haldane, J. B. S. (1956). The estimation and significance of the logarithm of a ratio of frequencies. Annals of Human Genetics, 20(4), 309-311.

Examples

# Compare Race1 vs Race2 at IQ bin 1 vs IQ bin 4 (reference)
lor_ci_2x2(a = 30, b = 25, c = 28, d = 24)

Individual-Level VM and Minority Group Dataset

Description

An individual-level dataset with N = 900 observations, reconstructed by row expansion from the vm_raw grouped data (SONSCA Analysis C). Each row represents one individual with their Vocabulary Meaning (VM) test score, discretised VM bin, binary minority/majority group indicator, and race group label.

Usage

lorbridge_data

Format

A data.frame with 900 rows and 4 columns:

VM

Numeric. Raw Vocabulary Meaning score (range 54-149).

VMbin

Factor. Discretised VM bin (VM1-VM6), with VM4 as the reference level. Breakpoints: <=64.28, <=81, <=100, <=121, <=138.36, >138.36.

minority

Integer. Binary outcome: 1 = minority (Race1 + Race2 + Race3), 0 = majority (Race4).

Race

Character. Race group label (Race1, Race2, Race3, Race4).

Source

Reconstructed from the vm_raw table in Kim, S.-K. (2026), unified SONSCA/DONSCA analysis script.

Multinomial Logistic Regression with CCMs (DONSCA Bridge)

Description

Fits a multinomial logistic regression model with a single standardised continuous predictor (per 1 SD), using a specified baseline outcome category. Returns log-odds ratios, odds ratios, Wald CIs, and the full set of CCMs for each non-baseline outcome level.

Usage

mlr_ccm(outcome, predictor, baseline = NULL, alpha = 0.05)

Arguments

Value

A data.frame with one row per non-baseline outcome level, containing LOR, OR, 95% CI, and CCMs (YuleQ, YuleY, r_meta).

Bootstrap Confidence Intervals for SONSCA Cosine Theta

Description

Generates bias-corrected and accelerated (BCa-style percentile) bootstrap confidence intervals for doubly-anchored cosine theta estimates from SONSCA. Resamples the contingency table under a multinomial model.

Usage

sonsca_bootstrap(
  tab,
  row_anchor,
  col_anchor,
  row_groups,
  col_groups,
  B = 2000,
  alpha = 0.05
)

Arguments

Value

A named list with elements:

point

Numeric vector of point estimates (flattened row x col).

lo

Lower CI bounds.

hi

Upper CI bounds.

boot_n

Number of successful bootstrap replications.

Pairwise CCMs for SONSCA

Description

Computes pairwise closeness-of-concordance measures (CCMs) for a single (row, column) contrast against the anchor (row_anchor, col_anchor) in a SONSCA contingency table.

Usage

sonsca_ccm(tab, row_k, bin_j, row_anchor, col_anchor, alpha = 0.05)

Arguments

Value

A one-row data.frame with columns: Race, Bin, and all CCM columns from ccm_row().

SONSCA Column-Isometric Coordinates

Description

Extracts row standard coordinates and column principal coordinates from a Singly-Ordered Nonsymmetric Correspondence Analysis (SONSCA) fit via CAvariants. This is the column-isometric scaling recommended for cosine theta computation.

Usage

sonsca_coords(tab)

Arguments

Value

A named list with elements:

row_coords

Row standard coordinate matrix (rows = row categories).

col_coords

Column principal coordinate matrix (rows = column categories).

Doubly-Anchored Cosine Theta for SONSCA

Description

Computes the matrix of doubly-anchored cosine theta values between all non-anchor row and column pairs in SONSCA coordinate space.

Usage

sonsca_cosines(row_coords, col_coords, row_anchor, col_anchor)

Arguments

Value

A matrix of cosine theta values with rows = non-anchor row categories and columns = non-anchor column categories. Anchor rows/ columns yield NA (zero displacement).

IQ Contingency Table (4 Races x 6 IQ Bins)

Description

A 4 x 6 contingency table cross-classifying four racial groups by six discretised IQ score bins. Used in SONSCA Analysis A.

Usage

tab_IQ

Format

A numeric matrix with 4 rows (Race1, Race2, Race3, Race4) and 6 columns (IQ1-IQ6).

Source

Kim, S.-K. (2026), unified SONSCA/DONSCA analysis script, Analysis A.

IQ x VM Contingency Table (6 IQ Bins x 6 VM Bins)

Description

A 6 x 6 contingency table cross-classifying six discretised IQ bins by six discretised Vocabulary Meaning (VM) bins. Both rows and columns are ordered, making this suitable for DONSCA.

Usage

tab_IQ_VM

Format

A numeric matrix with 6 rows (IQ1-IQ6) and 6 columns (VM1-VM6).

Source

Kim, S.-K. (2026), unified SONSCA/DONSCA analysis script, Analysis 3a.

VM Contingency Table (4 Races x 6 VM Bins)

Description

A 4 x 6 contingency table cross-classifying four racial groups by six discretised Vocabulary Meaning (VM) score bins. Used in SONSCA Analysis B.

Usage

tab_VM

Format

A numeric matrix with 4 rows (Race1, Race2, Race3, Race4) and 6 columns (VM1-VM6).

Source

Kim, S.-K. (2026), unified SONSCA/DONSCA analysis script, Analysis B.