---
title: "Use Case IDs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Use Case IDs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: references.bib
csl: apa.csl
---



Instead of using row numbers (`case.idx` in the `lavaan` object),
`lavaan_rerun()` from the
package [semfindr](https://sfcheung.github.io/semfindr/)
[@cheung_semfindr_2026]
supports user supplied case IDs.
This can make the output more readable.


``` r
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()`:


``` r
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`:




``` r
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:


``` r
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


``` r
fit_est_change <- est_change(fit_rerun)
#> Error:
#> ! object 'fit_rerun' not found
fit_est_change
#> Error:
#> ! object 'fit_est_change' not found
```

``` r
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


``` r
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


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

## Changes in Fit Measures


``` r
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


``` r
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


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

## Mahalanobis Distance


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

## Fit Measure vs. Generalized Cook's Distance


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

## Bubble Plot


``` r
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