Ivan Jacob Agaloos Pesigan 2024-10-22
Generates confidence intervals for standardized regression coefficients
using delta method standard errors for models fitted by lm() as
described in Yuan and Chan (2011:
http://doi.org/10.1007/s11336-011-9224-6) and Jones and Waller (2015:
http://doi.org/10.1007/s11336-013-9380-y). The package can also be
used to generate confidence intervals for differences of standardized
regression coefficients and as a general approach to performing the
delta method. A description of the package and code examples are
presented in Pesigan, Sun, and Cheung (2023:
https://doi.org/10.1080/00273171.2023.2201277).
You can install the CRAN release of betaDelta with:
install.packages("betaDelta")You can install the development version of betaDelta from
GitHub with:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("jeksterslab/betaDelta")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). Confidence intervals for the standardized
regression coefficients are generated using the BetaDelta() function
from the betaDelta package following Yuan & Chan (2011) and Jones &
Waller (2015).
library(betaDelta)df <- betaDelta::nas1982object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = df)BetaDelta(object, type = "mvn", alpha = 0.05)
#> Call:
#> BetaDelta(object = object, type = "mvn", alpha = 0.05)
#>
#> Standardized regression slopes with MVN standard errors:
#> est se t df p 2.5% 97.5%
#> NARTIC 0.4951 0.0759 6.5272 42 0.000 0.3421 0.6482
#> PCTGRT 0.3915 0.0770 5.0824 42 0.000 0.2360 0.5469
#> PCTSUPP 0.2632 0.0747 3.5224 42 0.001 0.1124 0.4141BetaDelta(object, type = "adf", alpha = 0.05)
#> Call:
#> BetaDelta(object = object, type = "adf", alpha = 0.05)
#>
#> Standardized regression slopes with ADF standard errors:
#> est se t df p 2.5% 97.5%
#> NARTIC 0.4951 0.0674 7.3490 42 0.0000 0.3592 0.6311
#> PCTGRT 0.3915 0.0710 5.5164 42 0.0000 0.2483 0.5347
#> PCTSUPP 0.2632 0.0769 3.4231 42 0.0014 0.1081 0.4184The package can also be used to generate confidence intervals for
differences of standardized regression coefficients using the
DiffBetaDelta() function. It can also be used as a general approach to
performing the delta method using the Delta() and DeltaGeneric()
functions.
To cite betaDelta in publications, please use:
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. https://doi.org/10.1080/00273171.2023.2201277
See GitHub Pages for package documentation.
To cite betaDelta in publications, please cite Pesigan et al. (2023).
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. https://doi.org/10.1007/s11336-013-9380-y
National Research Council. (1982). An assessment of research-doctorate programs in the United States: Social and behavioral sciences. National Academies Press. https://doi.org/10.17226/9781
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, 58(6), 1183–1186. https://doi.org/10.1080/00273171.2023.2201277
Yuan, K.-H., & Chan, W. (2011). Biases and standard errors of standardized regression coefficients. Psychometrika, 76(4), 670–690. https://doi.org/10.1007/s11336-011-9224-6