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

Last synced: 2 months ago
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A find(e)r of influential cases and outliers in SEM

• Host: GitHub
• URL: https://github.com/sfcheung/semfindr
• Owner: sfcheung
• License: gpl-3.0
• Created: 2020-06-12T02:44:37.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2025-03-23T09:41:18.000Z (3 months ago)
• Last Synced: 2025-03-30T01:51:47.755Z (3 months ago)
• Topics: diagnostics, influential-cases, lavaan, outlier-detection, r, r-package, sensitivity-analysis, structural-equation-modeling
• Language: R
• Homepage: https://sfcheung.github.io/semfindr/
• Size: 146 MB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE.md

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(Version 0.1.9, updated on 2025-03-04, [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`.