mctest: An R Package for Detection of Collinearity among Regressors

作者：Muhammad Imdadullah, Muhammad Aslam, Saima Altaf · 发表于：The R Journal · 年份：2016 · DOI：10.32614/rj-2016-062 · 被引用次数：154 · 研究领域：Advanced Statistical Methods and Models、Statistical Methods and Applications、Advanced Statistical Process Monitoring

It is common for linear regression models to be plagued with the problem of multicollinearity when two or more regressors are highly correlated.This problem results in unstable estimates of regression coefficients and causes some serious problems in validation and interpretation of the model.Different diagnostic measures are used to detect multicollinearity among regressors.Many statistical software and R packages provide few diagnostic measures for the judgment of multicollinearity.Most widely used diagnostic measures in these software are: coefficient of determination (R 2 ), variance inflation factor/tolerance limit (VIF/TOL), eigenvalues, condition number (CN) and condition index (CI) etc.In this manuscript, we present an R package, mctest, that computes popular and widely used multicollinearity diagnostic measures.The package also indicates which regressors may be the reason of collinearity among regressors. Brief introduction of collinearityConsider the conventional multiple linear regression equationwhere y is an n × 1 vector of observation on response variable, X is known design matrix of order n × p, β is an p × 1 vector of unknown parameters and u is an n × 1 vector of random errors with mean zero and variance σ 2 I n , where I n is an identity matrix of order n.One of the important assumptions of the classical linear regression model is that there is no exact collinearity among the regressors otherwise, the issue is referred to as multicollinearity.Generally, the p...