rmatReg: Regression

In allengoebl/iopsych: Methods for Industrial/Organizational Psychology

Description

Regression

Usage

rmatReg(r_mat, y_col, x_col, N = NULL, alpha = 0.05)

Arguments

r_mat	A correlation matrix.
y_col	The column representing the criterion variable.
x_col	A vector of columns representing predictor variables.
N	Number of observations
alpha	alpha value for (1 - alpha)% Confidence Interval

Value

Regression beta weights and R^2. If N is supplied, the standard error of the beta weights as well as the confidence intervals are returned as well.

Note

If N is non-null the function will compute corrected standard errors for the standardized regression coefficients using the delta method. For additional details on the calculation of the corrected standard errors see Yuan and Chan (2011) and Jones and Waller (2015).

Author(s)

Allen Goebl and Jeff Jones

References

Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free covariance matrix of standardized regression coefficients: Theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378.

Yuan, K. and Chan, W. (2011). Biases and standard errors of standardized regression coefficients. Psychometrika, 76, 670-690.

Examples

Rs <- matrix(c(1.0, 0.2,  0.3, 0.4, -0.4,
               0.2, 1.0,  0.5, 0.1,  0.1,
               0.3, 0.5,  1.0, 0.2, -0.3,
               0.4, 0.1,  0.2, 1.0,  0.4,
              -0.4, 0.1, -0.3, 0.4,  1.0), 5, 5)
ys <- 5
xs <- 1:4

rmatReg(Rs, ys, xs)

# Example with standard errors
rmatReg(Rs, ys, xs, N = 100)

allengoebl/iopsych documentation built on May 10, 2019, 9:22 a.m.