Estimate Multiple Correlation Coefficients (R-squared and adjusted R-squared) and the Corresponding Sampling Covariance Matrix

Estimate Multiple Correlation Coefficients (R-squared and adjusted R-squared) and the Corresponding Sampling Covariance Matrix

Usage

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

Arguments

object

Object of class betasandwich, that is, the output of the BetaHC(), BetaN(), or BetaADF() functions.

alpha

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

Value

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

call

Function call.

fit

The argument object.

args

Function arguments.

vcov

Sampling covariance matrix of multiple correlation coefficients (R-squared and adjusted R-squared).

est

Vector of multiple correlation coefficients (R-squared and adjusted R-squared).

See also

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

Author

Ivan Jacob Agaloos Pesigan

Examples

object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
# Methods -------------------------------------------------------
print(rsq)
#> Call:
#> RSqBetaSandwich(object = std)
#> 
#> Multiple correlation with HC3 standard errors:
#>        est     se      t df      p    0.05%     0.5%    2.5%  97.5%   99.5%
#> rsq 0.8045 3.9483 0.2038 42 0.8395 -13.1636  -9.8483 -7.1635 8.7725 11.4573
#> adj 0.7906 4.2303 0.1869 42 0.8527 -14.1753 -10.6231 -7.7466 9.3277 12.2043
#>      99.95%
#> rsq 14.7726
#> adj 15.7564
summary(rsq)
#> Call:
#> RSqBetaSandwich(object = std)
#> 
#> Multiple correlation with HC3 standard errors:
#>        est     se      t df      p    0.05%     0.5%    2.5%  97.5%   99.5%
#> rsq 0.8045 3.9483 0.2038 42 0.8395 -13.1636  -9.8483 -7.1635 8.7725 11.4573
#> adj 0.7906 4.2303 0.1869 42 0.8527 -14.1753 -10.6231 -7.7466 9.3277 12.2043
#>      99.95%
#> rsq 14.7726
#> adj 15.7564
coef(rsq)
#>   rsq.rsq   adj.adj 
#> 0.8045263 0.7905638 
vcov(rsq)
#>          rsq      adj
#> rsq 15.58911 16.70262
#> adj 16.70262 17.89567
confint(rsq, level = 0.95)
#>         2.5 %   97.5 %
#> rsq -7.163476 8.772529
#> adj -7.746582 9.327709