View source: R/betaSandwich-beta-mvn.R
BetaN | R Documentation |
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Assuming Multivariate Normality
BetaN(object, alpha = c(0.05, 0.01, 0.001))
object |
Object of class |
alpha |
Numeric vector.
Significance level |
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.
Returns an object
of class betasandwich
which is a list with the following elements:
Function call.
Function arguments.
Processed lm
object.
Asymptotic covariance matrix of the sample covariance matrix assuming multivariate normality.
Asymptotic covariance matrix HC correction.
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
Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. \Sexpr[results=rd]{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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2023.2201277")}
Other Beta Sandwich Functions:
BetaADF()
,
BetaHC()
,
DiffBetaSandwich()
,
RSqBetaSandwich()
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)
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