R: Standard Errors and CIs for Standardized Regression...

seBeta {bain} | R Documentation

## 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)

[Package bain version 0.2.6 Index]