View source: R/betaDelta-beta-delta.R
BetaDelta | R Documentation |
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix
BetaDelta(object, type = "mvn", alpha = c(0.05, 0.01, 0.001))
object |
Object of class |
type |
Character string.
If |
alpha |
Numeric vector.
Significance level |
Returns an object
of class betadelta
which is a list with the following elements:
Function call.
Function arguments.
Processed lm
object.
Asymptotic covariance matrix of the sample covariance matrix.
Asymptotic covariance matrix of the standardized slopes.
Sampling covariance matrix of the standardized slopes.
Vector of standardized slopes.
Ivan Jacob Agaloos Pesigan
Jones, J. A., & 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(2), 365–378. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-013-9380-y")}
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2023.2201277")}
Yuan, K.-H., & Chan, W. (2011). Biases and standard errors of standardized regression coefficients. Psychometrika, 76(4), 670–690. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-011-9224-6")}
Other Beta Delta Functions:
DiffBetaDelta()
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaDelta(object)
# Methods -------------------------------------------------------
print(std)
summary(std)
coef(std)
vcov(std)
confint(std, level = 0.95)
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