Interpreting Regression Effects

Description

The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights, structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.

Details

Author(s)

Kim Nimon <kim.nimon@gmail.com>, Fred L. Oswald, J. Kyle Roberts

References

Beaton, A. E. (1973) Commonality. (ERIC Document Reproduction Service No. ED111829)

Butts, C. T. (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.

Mood, A. M. (1969) Macro-analysis of the American educational system. Operations Research, 17, 770-784.

Nimon, K., Lewis, M., Kane, R. & Haynes, R. M. (2008) An R package to compute commonality coefficients in the multiple regression case: An introduction to the package and a practical example. Behavior Research Methods, 40(2), 457-466.

Nimon, K., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16, 650-674.

See Also

regr commonalityCoefficients canonCommonality calc.yhat boot.yhat booteval.yhat plotCI.yhat aps commonality dominance dombin rlw

Results