(Version 0.1.5.4, updated on 2023-08-30, release history)
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.
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 then compute case influence measures using the output of
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
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")).
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")).
The latest developmental version can be installed by
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
Please post your comments, suggestions, and bug reports as issues at GitHub, or contact the maintainer by email. Thanks in advance for trying out