Thirteen methods are denoted m1 to m13. Each yields p numbers when there are p regressors denoted xi. m1=OLS coefficient slopes. m2= t-stat of each slope. m3= beta coefficients OLS after all variables have mean zero and sd=1. m4= Pearson correlation coefficient between y and xi (only two variables at a time, assuming linearity). Let r*(y|xi) denote the generalized correlation coefficient allowing for nonlinearity from Vinod (2021, 2022). It does not equal analogous r*(xi|y). The larger of the two, max(r*(y|xi), r*(xi|y)), is given by the function depMeas() from the 'generalCorr' package. m5= depMeas, which allows nonlinearity. m5 is not comprehensive because it measures only two variables, y and xi, at a time. m6= generalized partial correlation coefficient or GPCC. This is the first comprehensive measure of practical significance. m7=a generalization of psychologists' "effect size" after incorporating the nonlinear effect of other variables. m8= local linear partial (dy/dxi) using the 'np' package for kernel regressions and local linear derivatives. m9= partial derivative (dy/dxi) using the 'NNS' package. m10=importance measure using NNS.boost() function of 'NNS.' m11=Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm measuring the importance of regressors.
pracSig13(y, bigx, yes13 = rep(1, 13), verbo = FALSE)output matrix (p x 13) containing m1 to m13 criteria (numerical measures of practical significance) along columns and a row for each regressor (excluding the intercept).
input dependent variable data as a vector
input matrix of p regressor variables
vector of ones to compute respective 13 measures m1 to m13. Default is all ones to compute all e.g., yes13[10]=0 means do not compute the m10 method.
logical to print results along the way default=FALSE
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
If m6, m10 slow down computations, we recommend setting yes13[6]=0=yes13[10] to turn off slowcomputation of m6 and m10 at least initially to get quick answers for other m's.
Vinod, H. D."Generalized Correlation and Kernel Causality with Applications in Development Economics" in Communications in Statistics -Simulation and Computation, 2015, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")
Vinod, H. D.", "Generalized Correlations and Instantaneous Causality for Data Pairs Benchmark," (March 8, 2015). https://www.ssrn.com/abstract=2574891
Vinod, H. D. “Generalized, Partial and Canonical Correlation Coefficients,” Computational Economics (2021) SpringerLink vol. 59, pp.1-28. URL https://link.springer.com/article/10.1007/s10614-021-10190-x
Vinod, H. D. “Kernel regression coefficients for practical significance," Journal of Risk and Financial Management 15(1), 2022 pp.1-13. https://doi.org/10.3390/jrfm15010032
Vinod, H. D.", "Hands-On Intermediate Econometrics Using R" (2022) World Scientific Publishers: Hackensack, NJ. https://www.worldscientific.com/worldscibooks/10.1142/12831
See Also as effSizCut,
See Also as reportRank