(Version 0.11.2, updated on 2024-06-05 release history)
This package includes functions for forming the likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling. It also supports the robust LBCI proposed by Falk (2018). It was described in the following manuscript:
- Cheung, S. F., & Pesigan, I. J. A. (2023). semlbci: An R package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters. Structural Equation Modeling: A Multidisciplinary Journal. 30(6), 985–999. https://doi.org/10.1080/10705511.2023.2183860
As argued in the article and by others, LBCI is usually better than Wald-based confidence interval and delta method confidence interval, which are the default method in most structural equation modeling (SEM) program. However, there is one technical disadvantage: LBCI cannot be directly computed but needs to be “found” (searched) by some algorithms. Wald CIs, on the other hand, can be computed quickly.
In semlbci, we try to address this disadvantage of LBCI by implementing an efficient method (illustrated by Pek & Wu, 2018, adapted from Wu & Neale, 2012), to help researchers to form LBCIs for model parameters, including user-defined parameters, in models fitted by lavaan. It can also form LBCIs for the standardized solution, such as “betas” (standardized regression coefficients) and correlations, and support multiple-group models. Last, it supports the robust LBCI proposed by Falk (2018) for nonnormal variables.
More information on this package can be found below:
How To Use It
Illustration with examples can be found in the Get Started guide (vignette("semlbci", package = "semlbci")).
Installation
The stable CRAN version can be installed by install.packages():
install.packages("semlbci")The latest version at GitHub can be installed by remotes::install_github():
remotes::install_github("sfcheung/semlbci")Implementation
It currently implements the algorithm illustrated by Pek and Wu (2018), adapted from Wu and Neale (2012) without adjustment for parameters with attainable bounds. It also supports the robust LBCI proposed by Falk (2018). More on the implementation can be found in the technical appendices.
References
Cheung, S. F., & Pesigan, I. J. A. (2023). semlbci: An R package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters. Structural Equation Modeling: A Multidisciplinary Journal. 30(6), 985–999. https://doi.org/10.1080/10705511.2023.2183860
Falk, C. F. (2018). Are robust standard errors the best approach for interval estimation with nonnormal data in structural equation modeling? Structural Equation Modeling: A Multidisciplinary Journal, 25(2), 244-266. https://doi.org/10.1080/10705511.2017.1367254
Pek, J., & Wu, H. (2015). Profile likelihood-based confidence intervals and regions for structural equation models. Psychometrika, 80(4), 1123-1145. https://doi.org/10.1007/s11336-015-9461-1
Wu, H., & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior Genetics, 42(6), 886-898. https://doi.org/10.1007/s10519-012-9560-z