Ivan Jacob Agaloos Pesigan 2024-05-05
Description
Generates nonparametric bootstrap confidence intervals (Efron & Tibshirani, 1993: https://doi.org/10.1201/9780429246593) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().
Installation
You can install the CRAN release of betaNB with:
install.packages("betaNB")You can install the development version of betaNB from GitHub with:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("jeksterslab/betaNB")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). Confidence intervals for the standardized regression coefficients are generated using the BetaNB() function from the betaNB package.
df <- betaNB::nas1982Regression
Fit the regression model using the lm() function.
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = df)Nonparametric Bootstrap
nb <- NB(object)Standardized Regression Slopes
BetaNB(nb, alpha = 0.05)
#> Call:
#> BetaNB(object = nb, alpha = 0.05)
#>
#> Standardized regression slopes
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.4951 0.0735 5000 0.3538 0.6383
#> PCTGRT 0.3915 0.0777 5000 0.2338 0.5400
#> PCTSUPP 0.2632 0.0787 5000 0.1130 0.4161Other Effect Sizes
The betaNB package also has functions to generate nonparametric bootstrap confidence intervals for other effect sizes such as RSqNB() for multiple correlation coefficients (R-squared and adjusted R-squared), DeltaRSqNB() for improvement in R-squared, SCorNB() for semipartial correlation coefficients, PCorNB() for squared partial correlation coefficients, and DiffBetaNB() for differences of standardized regression coefficients.
Multiple Correlation Coefficients (R-squared and adjusted R-squared)
RSqNB(nb, alpha = 0.05)
#> Call:
#> RSqNB(object = nb, alpha = 0.05)
#>
#> R-squared and adjusted R-squared
#> type = "pc"
#> est se R 2.5% 97.5%
#> rsq 0.8045 0.0524 5000 0.6922 0.8948
#> adj 0.7906 0.0561 5000 0.6703 0.8872Improvement in R-squared
DeltaRSqNB(nb, alpha = 0.05)
#> Call:
#> DeltaRSqNB(object = nb, alpha = 0.05)
#>
#> Improvement in R-squared
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.1859 0.0601 5000 0.0838 0.3154
#> PCTGRT 0.1177 0.0490 5000 0.0343 0.2231
#> PCTSUPP 0.0569 0.0338 5000 0.0098 0.1352Semipartial Correlation Coefficients
SCorNB(nb, alpha = 0.05)
#> Call:
#> SCorNB(object = nb, alpha = 0.05)
#>
#> Semipartial correlations
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.4312 0.0703 5000 0.2894 0.5616
#> PCTGRT 0.3430 0.0735 5000 0.1852 0.4723
#> PCTSUPP 0.2385 0.0706 5000 0.0992 0.3677Squared Partial Correlation Coefficients
PCorNB(nb, alpha = 0.05)
#> Call:
#> PCorNB(object = nb, alpha = 0.05)
#>
#> Squared partial correlations
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC 0.4874 0.0991 5000 0.2772 0.6681
#> PCTGRT 0.3757 0.1078 5000 0.1531 0.5809
#> PCTSUPP 0.2254 0.1140 5000 0.0457 0.4755Differences of Standardized Regression Coefficients
DiffBetaNB(nb, alpha = 0.05)
#> Call:
#> DiffBetaNB(object = nb, alpha = 0.05)
#>
#> Differences of standardized regression slopes
#> type = "pc"
#> est se R 2.5% 97.5%
#> NARTIC-PCTGRT 0.1037 0.1339 5000 -0.1532 0.3768
#> NARTIC-PCTSUPP 0.2319 0.1244 5000 -0.0060 0.4729
#> PCTGRT-PCTSUPP 0.1282 0.1276 5000 -0.1210 0.3735Documentation
See GitHub Pages for package documentation.