Title: | Robust Confidence Intervals for Standardized Regression Coefficients |
Version: | 1.0.7 |
Description: | Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi: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) <doi:10.1080/00273171.2023.2201277>. |
URL: | https://github.com/jeksterslab/betaSandwich, https://jeksterslab.github.io/betaSandwich/ |
BugReports: | https://github.com/jeksterslab/betaSandwich/issues |
License: | MIT + file LICENSE |
Encoding: | UTF-8 |
LazyData: | true |
Depends: | R (≥ 3.5.0) |
Suggests: | knitr, rmarkdown, testthat, betaDelta |
RoxygenNote: | 7.3.1 |
NeedsCompilation: | no |
Packaged: | 2024-04-14 16:43:47 UTC; root |
Author: | Ivan Jacob Agaloos Pesigan
|
Maintainer: | Ivan Jacob Agaloos Pesigan <r.jeksterslab@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2024-04-14 18:00:02 UTC |
betaSandwich: Robust Confidence Intervals for Standardized Regression Coefficients
Description
Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) doi: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) doi:10.1080/00273171.2023.2201277.
Author(s)
Maintainer: Ivan Jacob Agaloos Pesigan r.jeksterslab@gmail.com (ORCID) [copyright holder]
See Also
Useful links:
Report bugs at https://github.com/jeksterslab/betaSandwich/issues
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Using the Asymptotic Distribution-Free Approach
Description
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Using the Asymptotic Distribution-Free Approach
Usage
BetaADF(object, alpha = c(0.05, 0.01, 0.001))
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
Details
Note that while the calculation in BetaADF()
is different from betaDelta::BetaDelta()
with type = "adf"
,
the results are numerically equivalent.
BetaADF()
is appropriate when sample sizes are moderate to large
(n > 250
).
BetaHC()
is recommended in most situations.
Value
Returns an object
of class betasandwich
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- lm_process
Processed
lm
object.- gamma_n
Asymptotic covariance matrix of the sample covariance matrix assuming multivariate normality.
- gamma_hc
Asymptotic covariance matrix HC correction.
- gamma
Asymptotic covariance matrix of the sample covariance matrix.
- acov
Asymptotic covariance matrix of the standardized slopes.
- vcov
Sampling covariance matrix of the standardized slopes.
- est
Vector of standardized slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
References
Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37(1), 62–83. doi:10.1111/j.2044-8317.1984.tb00789.x
Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. 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. doi:10.1080/00273171.2023.2201277
See Also
Other Beta Sandwich Functions:
BetaHC()
,
BetaN()
,
DiffBetaSandwich()
,
RSqBetaSandwich()
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaADF(object)
# Methods -------------------------------------------------------
print(std)
summary(std)
coef(std)
vcov(std)
confint(std, level = 0.95)
Estimate Standardized Regression Coefficients and the Corresponding Robust Sampling Covariance Matrix Using the Heteroskedasticity Consistent Approach
Description
Estimate Standardized Regression Coefficients and the Corresponding Robust Sampling Covariance Matrix Using the Heteroskedasticity Consistent Approach
Usage
BetaHC(
object,
type = "hc3",
alpha = c(0.05, 0.01, 0.001),
g1 = 1,
g2 = 1.5,
k = 0.7
)
Arguments
object |
Object of class |
type |
Character string.
Correction type.
Possible values are
|
alpha |
Numeric vector.
Significance level |
g1 |
Numeric.
|
g2 |
Numeric.
|
k |
Numeric.
Constant |
Value
Returns an object
of class betasandwich
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- lm_process
Processed
lm
object.- gamma_n
Asymptotic covariance matrix of the sample covariance matrix assuming multivariate normality.
- gamma_hc
Asymptotic covariance matrix HC correction.
- gamma
Asymptotic covariance matrix of the sample covariance matrix.
- acov
Asymptotic covariance matrix of the standardized slopes.
- vcov
Sampling covariance matrix of the standardized slopes.
- est
Vector of standardized slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
References
Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. 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. doi:10.1080/00273171.2023.2201277
See Also
Other Beta Sandwich Functions:
BetaADF()
,
BetaN()
,
DiffBetaSandwich()
,
RSqBetaSandwich()
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
# Methods -------------------------------------------------------
print(std)
summary(std)
coef(std)
vcov(std)
confint(std, level = 0.95)
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Assuming Multivariate Normality
Description
Estimate Standardized Regression Coefficients and the Corresponding Sampling Covariance Matrix Assuming Multivariate Normality
Usage
BetaN(object, alpha = c(0.05, 0.01, 0.001))
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
Details
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.
Value
Returns an object
of class betasandwich
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- lm_process
Processed
lm
object.- gamma_n
Asymptotic covariance matrix of the sample covariance matrix assuming multivariate normality.
- gamma_hc
Asymptotic covariance matrix HC correction.
- gamma
Asymptotic covariance matrix of the sample covariance matrix.
- acov
Asymptotic covariance matrix of the standardized slopes.
- vcov
Sampling covariance matrix of the standardized slopes.
- est
Vector of standardized slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
References
Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. 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. doi:10.1080/00273171.2023.2201277
See Also
Other Beta Sandwich Functions:
BetaADF()
,
BetaHC()
,
DiffBetaSandwich()
,
RSqBetaSandwich()
Examples
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)
Estimate Differences of Standardized Slopes and the Corresponding Sampling Covariance Matrix
Description
Estimate Differences of Standardized Slopes and the Corresponding Sampling Covariance Matrix
Usage
DiffBetaSandwich(object, alpha = c(0.05, 0.01, 0.001))
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
Value
Returns an object of class diffbetasandwich
which is a list with the following elements:
- call
Function call.
- fit
The argument
object
.- args
Function arguments.
- vcov
Sampling covariance matrix of differences of standardized slopes.
- est
Vector of differences of standardized slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
See Also
Other Beta Sandwich Functions:
BetaADF()
,
BetaHC()
,
BetaN()
,
RSqBetaSandwich()
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
# Methods -------------------------------------------------------
print(diff)
summary(diff)
coef(diff)
vcov(diff)
confint(diff, level = 0.95)
Estimate Multiple Correlation Coefficients (R-squared and adjusted R-squared) and the Corresponding Sampling Covariance Matrix
Description
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 |
alpha |
Numeric vector.
Significance level |
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).
Author(s)
Ivan Jacob Agaloos Pesigan
See Also
Other Beta Sandwich Functions:
BetaADF()
,
BetaHC()
,
BetaN()
,
DiffBetaSandwich()
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
# Methods -------------------------------------------------------
print(rsq)
summary(rsq)
coef(rsq)
vcov(rsq)
confint(rsq, level = 0.95)
Standardized Regression Slopes
Description
Standardized Regression Slopes
Usage
## S3 method for class 'betasandwich'
coef(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a vector of standardized regression slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
coef(std)
Differences of Standardized Regression Slopes
Description
Differences of Standardized Regression Slopes
Usage
## S3 method for class 'diffbetasandwich'
coef(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a vector of differences of standardized regression slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
coef(diff)
Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Description
Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Usage
## S3 method for class 'rsqbetasandwich'
coef(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a vector of multiple correlation coefficients (R-squared and adjusted R-squared)
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
coef(rsq)
Confidence Intervals for Standardized Regression Slopes
Description
Confidence Intervals for Standardized Regression Slopes
Usage
## S3 method for class 'betasandwich'
confint(object, parm = NULL, level = 0.95, ...)
Arguments
object |
Object of class |
parm |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
the confidence level required. |
... |
additional arguments. |
Value
Returns a matrix of confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
confint(std, level = 0.95)
Confidence Intervals for Differences of Standardized Regression Slopes
Description
Confidence Intervals for Differences of Standardized Regression Slopes
Usage
## S3 method for class 'diffbetasandwich'
confint(object, parm = NULL, level = 0.95, ...)
Arguments
object |
Object of class |
parm |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
the confidence level required. |
... |
additional arguments. |
Value
Returns a matrix of confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
confint(diff, level = 0.95)
Confidence Intervals for Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Description
Confidence Intervals for Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Usage
## S3 method for class 'rsqbetasandwich'
confint(object, parm = NULL, level = 0.95, ...)
Arguments
object |
Object of class |
parm |
a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
the confidence level required. |
... |
additional arguments. |
Value
Returns a matrix of confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
confint(rsq, level = 0.95)
1982 National Academy of Sciences Doctoral Programs Data
Description
1982 National Academy of Sciences Doctoral Programs Data
Usage
nas1982
Format
Ratings of 46 doctoral programs in psychology in the USA with the following variables:
- QUALITY
Program quality ratings.
- NFACUL
Number of faculty members in the program.
- NGRADS
Number of program graduates.
- PCTSUPP
Percentage of program graduates who received support.
- PCTGRT
Percent of faculty members holding research grants.
- NARTIC
Number of published articles attributed to program faculty member.
- PCTPUB
Percent of faculty with one or more published article.
References
National Research Council. (1982). An assessment of research-doctorate programs in the United States: Social and behavioral sciences. doi:10.17226/9781. Reproduced with permission from the National Academy of Sciences, Courtesy of the National Academies Press, Washington, D.C.
Print Method for an Object of Class betasandwich
Description
Print Method for an Object of Class betasandwich
Usage
## S3 method for class 'betasandwich'
print(x, alpha = NULL, digits = 4, ...)
Arguments
x |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of standardized regression slopes, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
print(std)
Print Method for an Object of Class diffbetasandwich
Description
Print Method for an Object of Class diffbetasandwich
Usage
## S3 method for class 'diffbetasandwich'
print(x, alpha = NULL, digits = 4, ...)
Arguments
x |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of differences of standardized regression slopes, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
print(diff)
Print Method for an Object of Class rsqbetasandwich
Description
Print Method for an Object of Class rsqbetasandwich
Usage
## S3 method for class 'rsqbetasandwich'
print(x, alpha = NULL, digits = 4, ...)
Arguments
x |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of multiple correlation coefficients (R-squared and adjusted R-squared), standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
print(rsq)
Summary Method for an Object of Class betasandwich
Description
Summary Method for an Object of Class betasandwich
Usage
## S3 method for class 'betasandwich'
summary(object, alpha = NULL, digits = 4, ...)
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of standardized regression slopes, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
summary(std)
Summary Method for an Object of Class diffbetasandwich
Description
Summary Method for an Object of Class diffbetasandwich
Usage
## S3 method for class 'diffbetasandwich'
summary(object, alpha = NULL, digits = 4, ...)
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of differences of standardized regression slopes, standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
summary(diff)
Summary Method for an Object of Class rsqbetasandwich
Description
Summary Method for an Object of Class rsqbetasandwich
Usage
## S3 method for class 'rsqbetasandwich'
summary(object, alpha = NULL, digits = 4, ...)
Arguments
object |
Object of class |
alpha |
Numeric vector.
Significance level |
digits |
Digits to print. |
... |
additional arguments. |
Value
Returns a matrix of multiple correlation coefficients (R-squared and adjusted R-squared), standard errors, test statistics, degrees of freedom, p-values, and confidence intervals.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
summary(rsq)
Sampling Covariance Matrix of the Standardized Regression Slopes
Description
Sampling Covariance Matrix of the Standardized Regression Slopes
Usage
## S3 method for class 'betasandwich'
vcov(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a matrix of the variance-covariance matrix of standardized slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
vcov(std)
Sampling Covariance Matrix of Differences of Standardized Regression Slopes
Description
Sampling Covariance Matrix of Differences of Standardized Regression Slopes
Usage
## S3 method for class 'diffbetasandwich'
vcov(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a matrix of the variance-covariance matrix of differences of standardized regression slopes.
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
diff <- DiffBetaSandwich(std)
vcov(diff)
Sampling Covariance Matrix of Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Description
Sampling Covariance Matrix of Multiple Correlation Coefficients (R-Squared and Adjusted R-Squared)
Usage
## S3 method for class 'rsqbetasandwich'
vcov(object, ...)
Arguments
object |
Object of class |
... |
additional arguments. |
Value
Returns a matrix of the variance-covariance matrix of multiple correlation coefficients (R-squared and adjusted R-squared).
Author(s)
Ivan Jacob Agaloos Pesigan
Examples
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
std <- BetaHC(object)
rsq <- RSqBetaSandwich(std)
vcov(rsq)