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https://github.com/sfcheung/semfindr

A find(e)r of influential cases and outliers in SEM
https://github.com/sfcheung/semfindr

diagnostics influential-cases lavaan outlier-detection r r-package sensitivity-analysis structural-equation-modeling

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A find(e)r of influential cases and outliers in SEM

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(Version 0.1.8.1, updated on 2024-06-06, [release history](https://sfcheung.github.io/semfindr/news/index.html))



# semfindr: Finding influential cases in SEM 



A find(e)r of influential cases in structural equation modeling

based mainly on the sensitivity analysis procedures presented by Pek and

MacCallum (2011).



This package supports two approaches: leave-one-out analysis and approximate

case influence.



## Leave-One-Out Analysis



This approach examines the influence of each case by refitting a model with

this case removed.



Unlike other similar

packages, the workflow adopted in semfindr separates the leave-one-out

analysis (refitting a model with one case removed) from the case influence

measures.



- Users first do the leave-one-out model fitting for all cases, or

cases selected based on some criteria

(`vignette("selecting_cases", package = "semfindr")`), using

`lavaan_rerun()`.



- Users then compute case influence measures

using the output of `lavaan_rerun()`.



This approaches avoids unnecessarily refitting the models for each set of

influence measures, and also allows analyzing only probable influential cases

when the model takes a long time to fit.



The functions were designed to be flexible

such that users can compute case influence measures such as



- standardized parameter estimates and generalized Cook's distance for

  selected parameters;

- changes in raw or standardized estimates of parameters;

- changes in fit measures supported by `lavaan::fitMeasures()`.



This package can also be generate plots to visualize

case influence, including a bubble plot similar to that by `car::influencePlot()`

All plots generated are `ggplot` plots that can be further modified by users.

More can be found in *Quick Start* (`vignette("semfindr", package = "semfindr")`).



## Approximate Case Influence



This approach computes the approximate influence of each case using *casewise*

*scores* and *casewise* *likelihood*. This method is efficient because it does

not requires refitting the model for each case. However, it can only approximate

the influence, unlike the leave-one-out approach, which produce exact influence.

This approach can be used when the number of cases is very large

and/or the model takes a long time to fit. Technical details can be found in the

vignette *Approximate Case Influence Using Scores and Casewise Likelihood*

(`vignette("casewise_scores", package = "semfindr")`).



# Installation



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



```r

install.packages("semfindr")

```



The latest developmental version can be installed by `remotes::install_github`:



```r

remotes::install_github("sfcheung/semfindr")

```



You can learn more about this package at the

[Github page](https://sfcheung.github.io/semfindr/) of this

package and

Quick Start (`vignette("semfindr", package = "semfindr")`).



# Reference



Pek, J., & MacCallum, R. (2011). Sensitivity analysis in structural equation

models: Cases and their influence. *Multivariate Behavioral Research, 46*(2),

202-228. https://doi.org/10.1080/00273171.2011.561068



# Comments, Suggestions, and Bug Reports



Please post your comments, suggestions, and bug reports as issues

at [GitHub](https://github.com/sfcheung/semptools/issues), or contact

the maintainer by email. Thanks in advance for trying out `semfindr`.