R: Compute thirteen measures of practical significance

pracSig13 {practicalSigni}

R Documentation

Compute thirteen measures of practical significance

Description

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.

Usage

pracSig13(y, bigx, yes13 = rep(1, 13), verbo = FALSE)

Arguments

`y`	input dependent variable data as a vector
`bigx`	input matrix of p regressor variables
`yes13`	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.
`verbo`	logical to print results along the way default=FALSE

Details

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.

Value

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).

Note

needs the function kern(), which requires package 'np'. also needs 'NNS', 'randomForest', packages.

The machine learning methods are subject to random seeds. For some seed values, m10 values from NNS.boost() become degenerate and are reported as NA or missing. In that case the average ranking output r613 from reportRank() needs manual adjustments.

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

Vinod, H. D."Generalized Correlation and Kernel Causality with Applications in Development Economics" in Communications in Statistics -Simulation and Computation, 2015, 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