seBetaCor {fungible} | R Documentation |
Standard Errors and CIs for Standardized Regression Coefficients from Correlations
Description
Computes Normal Theory and ADF Standard Errors and CIs for Standardized Regression Coefficients from Correlations
Usage
seBetaCor(R, rxy, Nobs, alpha = 0.05, digits = 3, covmat = "normal")
Arguments
R |
A p x p predictor correlation matrix. |
rxy |
A p x 1 vector of predictor-criterion correlations |
Nobs |
Number of observations. |
alpha |
Desired Type I error rate; default = .05. |
digits |
Number of significant digits to print; default = 3. |
covmat |
String = 'normal' (the default) or a (p+1)p/2 x (p+1)p/2 covariance matrix of correlations. The default option computes an asymptotic covariance matrix under the assumption of multivariate normal data. Users can supply a covariance matrix under asymptotic distribution free (ADF) or elliptical distributions when available. |
Value
cov.Beta |
Covariance matrix of standardized regression coefficients. |
se.Beta |
Vector of standard errors for the standardized regression coefficients. |
alpha |
Type-I error rate. |
CI.Beta |
(1-alpha)% confidence intervals for standardized regression coefficients. |
Author(s)
Jeff Jones and Niels Waller
References
Jones, J. A, and Waller, N. G. (2013). The Normal-Theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: Theoretical extensions and finite sample behavior.Technical Report (052913)[TR052913]
Nel, D.A.G. (1985). A matrix derivation of the asymptotic covariance matrix of sample correlation coefficients. Linear Algebra and its Applications, 67, 137-145.
Yuan, K. and Chan, W. (2011). Biases and standard errors of standardized regression coefficients. Psychometrika, 76(4), 670–690.
Examples
R <- matrix(c(1.0000, 0.3511, 0.3661,
0.3511, 1.0000, 0.4359,
0.3661, 0.4359, 1.0000), 3, 3)
rxy <- c(0.5820, 0.6997, 0.7621)
Nobs <- 46
out <- seBetaCor(R = R, rxy = rxy, Nobs = Nobs)
# 95% CIs for Standardized Regression Coefficients:
#
# lbound estimate ubound
# beta_1 0.107 0.263 0.419
# beta_2 0.231 0.391 0.552
# beta_3 0.337 0.495 0.653