betaSandwich (version 1.0.8)

BetaN: Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Assuming Multivariate Normality

Description

Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Assuming Multivariate Normality

Usage

BetaN(object, alpha = c(0.05, 0.01, 0.001))

Value

Returns an object of class betasandwich which is a list with the following elements:

call

Function call.

args

Function arguments.

lm_process

Processed lm object.

gamma_n

Asymptotic covariance matrix of the sample covariance matrix assuming multivariate normality.

gamma_hc

Asymptotic covariance matrix HC correction.

gamma

Asymptotic covariance matrix of the sample covariance matrix.

acov

Asymptotic covariance matrix of the standardized slopes.

vcov

Sampling covariance matrix of the standardized slopes.

est

Vector of standardized slopes.

Arguments

object

Object of class lm.

alpha

Numeric vector. Significance level \(\alpha\).

Author

Ivan Jacob Agaloos Pesigan

Details

Note that while the calculation in BetaN() is different from betaDelta::BetaDelta() with type = "mvn", the results are numerically equivalent. BetaN() assumes multivariate normality. BetaHC() is recommended in most situations.

References

Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. tools:::Rd_expr_doi("10.1007/s11336-017-9563-z")

Pesigan, I. J. A., Sun, R. W., & Cheung, S. F. (2023). betaDelta and betaSandwich: Confidence intervals for standardized regression coefficients in R. Multivariate Behavioral Research. tools:::Rd_expr_doi("10.1080/00273171.2023.2201277")

See Also

Other Beta Sandwich Functions: BetaADF(), BetaHC(), DiffBetaSandwich(), RSqBetaSandwich()

Examples

object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaN(object)
# Methods -------------------------------------------------------
print(std)
summary(std)
coef(std)
vcov(std)
confint(std, level = 0.95)