Package 'regcorr'

Title: Models of Pearson Correlation Coefficient
Description: Implements regression models to assess the effect of covariates on the Pearson correlation coefficient for both bivariate normal and bivariate binary responses. This package provides likelihood-based inference using Newton-Raphson estimation and bootstrap-based methods for significance testing, featuring built-in robust handling for numerical instabilities in extreme samples.
Authors: Ze Lin, Bo Li, Jinyao Shen
Maintainer: Ze Lin <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0
Built: 2026-06-04 08:39:52 UTC
Source: https://github.com/lonze-nb/regcorr

Help Index

Generate data from bivariate Bernoulli

Description

Generate data from bivariate Bernoulli

Usage

genDataBB(numSample, p, betaTrue, eta1True, eta2True, link)

Arguments

numSample

Sample size.
p

Number of covariates.
betaTrue

True beta vector.
eta1True

True eta1 vector.
eta2True

True eta2 vector.
link

Link function indicator ("1" = logistic; "2" = tanh).

Value

A list containing X, Y, and rho.

Generate data from bivariate normal

Description

Generate data from bivariate normal

Usage

genDataBN(numSample, p, betaTrue, eta1True, eta2True, link)

Arguments

numSample

Sample size.
p

Number of covariates.
betaTrue

True beta vector.
eta1True

True eta1 vector.
eta2True

True eta2 vector.
link

Link function indicator ("1" = logistic; "2" = tanh).

Value

A list containing X, Y, and rho.

Logistic function

Description

Logistic function

Usage

logistic(x)

Arguments

x

A numeric vector.

Value

The calculated logistic probability.

Estimate beta for Bivariate Bernoulli responses using Newton Raphson

Description

Estimate beta for Bivariate Bernoulli responses using Newton Raphson

Usage

NRfitBivBernoulli(Y, X, beta0, link)

Arguments

Y

n by 2 matrix, paired responses.
X

n by p matrix, covariate matrix including first column of ones.
beta0

Initial estimate of beta.
link

Indicator of link function ("1" = logistic, "2" = tanh).

Value

A list containing betaCurrent, numIter, and restart.

Estimate beta for Bivariate Normal responses using Newton Raphson

Description

Estimate beta for Bivariate Normal responses using Newton Raphson

Usage

NRfitBivNormal(Y, X, betaIni, link)

Arguments

Y

n by 2 matrix, paired responses.
X

n by p matrix, covariate matrix including first column of ones.
betaIni

Initial estimate of beta.
link

Indicator of link function ("1" = logistic, "2" = tanh).

Value

A list containing betaCurrent, numIter, and restart.

Generate bivariate binary data

Description

Generate bivariate binary data

Usage

rbinary(n, p, rho)

Arguments

n

Number of rows.
p

1 by 2 mean vector of bivariate variables.
rho

Correlation of bivariate variables.

Value

n by 2 matrix of generated binary variables.

Subroutine to test the significance of individual parameters

Description

Subroutine to test the significance of individual parameters

Usage

subRoutineTest(
  numSample,
  p,
  link,
  model,
  betaTrue,
  betaIni,
  eta1True,
  eta2True,
  numSimu,
  numBoot
)

Arguments

numSample

Sample size.
p

Number of covariates.
link

Link function.
model

Model type (1: biv normal; 2: biv Bernoulli).
betaTrue

True beta.
betaIni

Initial beta.
eta1True

True eta1.
eta2True

True eta2.
numSimu

Number of simulations.
numBoot

Number of bootstrap iterations.

Value

A list containing RMSE, ConsistRate, and testing power.