| Type: | Package |
| Title: | Generalized Hybrid Contrast Coding in Linear Models |
| Version: | 0.1.0 |
| Description: | Implements generalized hybrid contrast coding methods for K-level categorical predictors in linear models as established by Obulezi (2026)[cite: 3]. The package automates design matrix construction mixing dummy indicators and composite group contrasts, checks rank constraints via singular value decomposition, computes closed-form OLS parameter mappings, and estimates robust heteroscedastic covariance matrices[cite: 3]. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Depends: | R (≥ 4.0.0) |
| Imports: | stats |
| Suggests: | testthat (≥ 3.0.0) |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-08-03 16:38:47 UTC; Dr. O. J. Obulezi |
| Author: | Okechukwu J. Obulezi [aut, cre] |
| Maintainer: | Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng> |
| Repository: | CRAN |
| Date/Publication: | 2026-08-08 13:00:06 UTC |
Generate Generalized Hybrid Design Matrix
Description
Constructs a non-orthogonal, full-rank hybrid design matrix for qualitative factors with K >= 3 levels, combining dummy indicator columns with composite simple contrast vectors.
Usage
build_ghc_matrix(K, n, tol = 1e-12)
Arguments
K |
Positive integer. Total number of factor levels (K >= 3). |
n |
Integer vector of length K giving sub-sample sizes per group. |
tol |
Numeric tolerance threshold for SVD singular values to verify rank. |
Value
A numeric design matrix of size N x K (where N = sum(n)) with class
attribute "ghc_matrix" and matrix attribute "C_operator".
Examples
XH <- build_ghc_matrix(K = 3, n = c(10, 10, 10))
attr(XH, "condition_number")
Fit Linear Models via Generalized Hybrid Contrast Coding
Description
Computes linear regression parameters using analytical hybrid contrast mappings. Provides theoretical variance-covariance estimators under both homoscedasticity and heteroscedasticity.
Usage
ghc_lm(formula, data, robust = FALSE)
Arguments
formula |
Object of class |
data |
Data frame containing continuous response and categorical predictor. |
robust |
Logical. If TRUE, computes Huber-White sandwich variance. |
Value
An S3 object of class "ghc_lm" containing fitted metrics.
Examples
set.seed(123)
dat <- data.frame(
group = factor(rep(1:3, each = 10)),
y = rnorm(30, mean = 50, sd = 5)
)
mod <- ghc_lm(y ~ group, data = dat)
summary(mod)