seBeta {bain}

Standard Errors and CIs for Standardized Regression Coefficients

Description

Computes Normal Theory and ADF Standard Errors and CIs for Standardized Regression Coefficients

Usage

seBeta(
  X = NULL,
  y = NULL,
  cov.x = NULL,
  cov.xy = NULL,
  var.y = NULL,
  Nobs = NULL,
  alpha = 0.05,
  estimator = "ADF"
)

Arguments

X

Matrix of predictor scores.

y

Vector of criterion scores.

cov.x

Covariance or correlation matrix of predictors.

cov.xy

Vector of covariances or correlations between predictors and criterion.

var.y

Criterion variance.

Nobs

Number of observations.

alpha

Desired Type I error rate; default = .05.

estimator

'ADF' or 'Normal' confidence intervals - requires raw X and raw y; default = 'ADF'.

Value

cov.Beta

Normal theory or ADF covariance matrix of standardized regression coefficients.

se.Beta

standard errors for standardized regression coefficients.

alpha

desired Type-I error rate.

CI.Beta

Normal theory or ADF (1-alpha) intervals for standardized regression coefficients.

estimator

estimator = "ADF" or "Normal".

Author(s)

Jeff Jones and Niels Waller

References

Jones, J. A, and Waller, N. G. (2015). The Normal-Theory and Asymptotic Distribution-Free (ADF) covariance matrix of standardized regression coefficients: Theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378.

Examples

set.seed(123)

R <- matrix(.5, 3, 3)
diag(R) <- 1
X <- sesamesim[, c("peabody", "prenumb", "postnumb")]
y <- sesamesim$age
results <- seBeta(X, y, Nobs = nrow(sesamesim), alpha = .05, estimator = 'ADF')
print(results, digits = 3)

library(MASS)

set.seed(123)

R <- matrix(.5, 3, 3)
diag(R) <- 1
X <- mvrnorm(n = 200, mu = rep(0, 3), Sigma = R, empirical = TRUE)
Beta <- c(.2, .3, .4)
y <- X %*% Beta + .64 * scale(rnorm(200))
results <- seBeta(X, y, Nobs = 200, alpha = .05, estimator = 'ADF')
print(results, digits = 3)