Ivan Jacob Agaloos Pesigan 2024-10-22
Description
Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm()
as described in Dudgeon (2017: http://doi.org/10.1007/s11336-017-9563-z). The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023: https://doi.org/10.1080/00273171.2023.2201277).
Installation
You can install the CRAN release of betaSandwich
with:
install.packages("betaSandwich")
You can install the development version of betaSandwich
from GitHub with:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("jeksterslab/betaSandwich")
Example
In this example, a multiple regression model is fitted using program quality ratings (QUALITY
) as the regressand/outcome variable and number of published articles attributed to the program faculty members (NARTIC
), percent of faculty members holding research grants (PCTGRT
), and percentage of program graduates who received support (PCTSUPP
) as regressor/predictor variables using a data set from 1982 ratings of 46 doctoral programs in psychology in the USA (National Research Council, 1982). Robust confidence intervals for the standardized regression coefficients are generated using the BetaHC()
function from the betaSandwich
package following Dudgeon (2017).
df <- betaSandwich::nas1982
Fit the regression model using the lm()
function.
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = df)
Estimate the standardized regression slopes and the corresponding robust sampling covariance matrix.
BetaHC(object, type = "hc3", alpha = 0.05)
#> Call:
#> BetaHC(object = object, type = "hc3", alpha = 0.05)
#>
#> Standardized regression slopes with HC3 standard errors:
#> est se t df p 2.5% 97.5%
#> NARTIC 0.4951 0.0786 6.3025 42 0.0000 0.3366 0.6537
#> PCTGRT 0.3915 0.0818 4.7831 42 0.0000 0.2263 0.5567
#> PCTSUPP 0.2632 0.0855 3.0786 42 0.0037 0.0907 0.4358
Other Features
The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients.
Documentation
See GitHub Pages for package documentation.