| Title: | Generalized Measure of Correlation (GMC) |
|---|---|
| Description: | Provides tools to compute the Generalized Measure of Correlation (GMC), a dependence measure accounting for nonlinearity and asymmetry in the relationship between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>. |
| Authors: | Xuejing Ding [aut, cre], Zhengjun Zhang [aut] |
| Maintainer: | Xuejing Ding <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.2 |
| Built: | 2026-05-29 10:26:27 UTC |
| Source: | https://github.com/cran/GMC |
Feature selection using GMC ranking
GMC_feature_ranking(X, Y, kernel = dnorm, sort = TRUE)GMC_feature_ranking(X, Y, kernel = dnorm, sort = TRUE)
X |
A matrix or data.frame of predictors |
Y |
A numeric response vector |
kernel |
Kernel function (default = dnorm) |
sort |
Logical, whether to sort variables by GMC score |
A data.frame with variable names and GMC scores
# Generate sample data with multiple predictors set.seed(123) n <- 500 X1 <- rnorm(n) X2 <- rnorm(n) X3 <- rnorm(n) Y <- 2 * X1 + X2^2 + rnorm(n, sd = 0.5) X <- cbind(X1, X2, X3) # Rank features by GMC ranking <- GMC_feature_ranking(X, Y) print(ranking)# Generate sample data with multiple predictors set.seed(123) n <- 500 X1 <- rnorm(n) X2 <- rnorm(n) X3 <- rnorm(n) Y <- 2 * X1 + X2^2 + rnorm(n, sd = 0.5) X <- cbind(X1, X2, X3) # Rank features by GMC ranking <- GMC_feature_ranking(X, Y) print(ranking)
Generalized Measure of Correlation: GMC(X | Y)
GMC_X_given_Y(X, Y, kernel = dnorm)GMC_X_given_Y(X, Y, kernel = dnorm)
X |
Predictor variable |
Y |
Response variable |
kernel |
Kernel function (default = dnorm) |
GMC(X|Y) estimate
# Generate sample data with nonlinear relationship set.seed(123) n <- 1000 X <- rnorm(n) Y <- X^2 + rnorm(n, sd = 0.5) # Calculate GMC(X|Y) gmc_result <- GMC_X_given_Y(X, Y) print(gmc_result)# Generate sample data with nonlinear relationship set.seed(123) n <- 1000 X <- rnorm(n) Y <- X^2 + rnorm(n, sd = 0.5) # Calculate GMC(X|Y) gmc_result <- GMC_X_given_Y(X, Y) print(gmc_result)
Generalized Measure of Correlation: GMC(Y | X)
GMC_Y_given_X(X, Y, kernel = dnorm)GMC_Y_given_X(X, Y, kernel = dnorm)
X |
Predictor variable |
Y |
Response variable |
kernel |
Kernel function (default = dnorm) |
GMC(Y|X) estimate
# Generate sample data with linear relationship set.seed(123) n <- 1000 X <- rnorm(n) Y <- 2 * X + rnorm(n, sd = 0.5) # Calculate GMC(Y|X) gmc_result <- GMC_Y_given_X(X, Y) print(gmc_result)# Generate sample data with linear relationship set.seed(123) n <- 1000 X <- rnorm(n) Y <- 2 * X + rnorm(n, sd = 0.5) # Calculate GMC(Y|X) gmc_result <- GMC_Y_given_X(X, Y) print(gmc_result)