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https://github.com/sfcheung/semlbci

Find the likelihood based confidence intervals for parameters in structural equation modeling
https://github.com/sfcheung/semlbci

confidence-intervals lavaan likelihood-based profile-likelihood r-package structural-equation-modeling

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Find the likelihood based confidence intervals for parameters in structural equation modeling

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README 

        

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[![DOI](https://img.shields.io/badge/doi-10.1080/10705511.2023.2183860-blue.svg)](https://doi.org/10.1080/10705511.2023.2183860)



(Version 0.11.2, updated on 2024-06-05 [release history](https://sfcheung.github.io/semlbci/news/index.html))



# semlbci 



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)](https://doi.org/10.1080/10705511.2017.1367254).

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`](https://sfcheung.github.io/semlbci/),

we try to address this disadvantage of

LBCI by implementing an efficient

method (illustrated by

[Pek & Wu, 2018](https://doi.org/10.1007/s11336-015-9461-1),

adapted from [Wu & Neale, 2012](https://doi.org/10.1007/s10519-012-9560-z)),

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)](https://doi.org/10.1080/10705511.2017.1367254)

for nonnormal variables.



More information on this package can be found below:



https://sfcheung.github.io/semlbci/



# How To Use It



Illustration with examples can be found

in the [*Get Started* guide](https://sfcheung.github.io/semlbci/articles/semlbci.html)

(`vignette("semlbci", package = "semlbci")`).



# Installation



The stable CRAN version can be installed by `install.packages()`:



```r

install.packages("semlbci")

```



The latest version at GitHub can be installed by `remotes::install_github()`:



```r

remotes::install_github("sfcheung/semlbci")

```



# Implementation



It currently implements the

algorithm illustrated by [Pek and Wu (2018)](https://doi.org/10.1007/s11336-015-9461-1),

adapted from

[Wu and Neale (2012)](https://doi.org/10.1007/s10519-012-9560-z) 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](https://sfcheung.github.io/semlbci/articles/).



# 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



# Issues



If you have any suggestions or found any bugs or limitations, please feel

feel to open a GitHub issue. Thanks.



https://github.com/sfcheung/semlbci/issues