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Use Case IDs

Instead of using row numbers (case.idx in the lavaan object), lavaan_rerun() from the package semfindr (Cheung & Lai, 2026) supports user supplied case IDs. This can make the output more readable.

library(semfindr)
dat <- pa_dat
# Add case id
dat <- cbind(id = paste0("case", seq_len(nrow(dat))), dat)
head(dat)
#>      id          m1         dv        iv1         iv2
#> 1 case1  0.32067106  1.4587148  0.2055776 -0.42187811
#> 2 case2  0.15360231 -0.3809220  0.1853543  0.15229953
#> 3 case3  0.35136439 -0.4886773  0.9151424  1.16670950
#> 4 case4 -0.56529330 -0.9766142  0.2884440  0.04563409
#> 5 case5 -1.60657017 -1.0948066 -0.5756171 -0.18184854
#> 6 case6  0.03143301  0.5859886  0.1420111  0.06286986

Suppose that the data set has a column of case IDs. A model is fitted to this data set using lavaan::sem():

mod <-
"
m1 ~ iv1 + iv2
dv ~ m1
"
library(lavaan)
fit <- sem(mod, dat)

Rerun n Times

We refit the model 100 times, each time with one case removed. Although the id column is not stored in lavaan, it can be supplied through the argument case_id:

fit_rerun <- lavaan_rerun(fit,
                          case_id = dat$id)
#> The expected CPU time is 10.5 second(s).
#> Could be faster if run in parallel.
#> Error:
#> ! lavaan->lav_lavdata():  
#>    data= argument is not a data.frame, but of class 'numeric'
#> Timing stopped at: 0.03 0 0.03

The list of reruns now uses id as the names:

head(fit_rerun$rerun[1:3])
#> Error:
#> ! object 'fit_rerun' not found

As shown below, most diagnostic functions will use user supplied case IDs in their displays, making it easier to locate them in the original data set.

Diagnostic Functions

Standardized Changes in Estimates

fit_est_change <- est_change(fit_rerun)
#> Error:
#> ! object 'fit_rerun' not found
fit_est_change
#> Error:
#> ! object 'fit_est_change' not found
fit_est_change_paths_only <- est_change(fit_rerun,
                                parameters = c("m1 ~ iv1",
                                               "m1 ~ iv2",
                                               "dv ~ m1"))
#> Error:
#> ! object 'fit_rerun' not found
fit_est_change_paths_only
#> Error:
#> ! object 'fit_est_change_paths_only' not found

Raw Changes in Estimates

fit_est_change_raw <- est_change_raw(fit_rerun)
#> Error:
#> ! object 'fit_rerun' not found
fit_est_change_raw
#> Error:
#> ! object 'fit_est_change_raw' not found

Mahalanobis Distance

fit_md <- mahalanobis_rerun(fit_rerun)
#> Error:
#> ! object 'fit_rerun' not found
fit_md
#> Error:
#> ! object 'fit_md' not found

Changes in Fit Measures

fit_mc <- fit_measures_change(fit_rerun,
            fit_measures = c("chisq", "cfi", "tli", "rmsea"))
#> Error:
#> ! object 'fit_rerun' not found
fit_mc
#> Error:
#> ! object 'fit_mc' not found

All-In-One-Function

fit_influence <- influence_stat(fit_rerun)
#> Error:
#> ! object 'fit_rerun' not found
fit_influence
#> Error:
#> ! object 'fit_influence' not found

Diagnostic Plots

Generalized Cook’s Distance

gcd_plot(fit_influence, largest_gcd = 3)
#> Error:
#> ! object 'fit_influence' not found

Mahalanobis Distance

md_plot(fit_influence,
        largest_md = 3)
#> Error:
#> ! object 'fit_influence' not found

Fit Measure vs. Generalized Cook’s Distance

gcd_gof_plot(fit_influence,
             fit_measure = "rmsea",
             largest_gcd = 3,
             largest_fit_measure = 3)
#> Error:
#> ! object 'fit_influence' not found

Bubble Plot

gcd_gof_md_plot(fit_influence,
                fit_measure = "rmsea",
                largest_gcd = 3,
                largest_fit_measure = 3,
                largest_md = 3,
                circle_size = 15)
#> Error:
#> ! object 'fit_influence' not found

References

Cheung, S. F., & Lai, M. H. C. (2026). semfindr: An R package for identifying influential cases in structural equation modeling. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2026.2634293

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.