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Use Case IDs

Instead of using row numbers (case.idx in the lavaan object), lavaan_rerun() from the package semfindr 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 list of reruns now uses id as the names:

head(fit_rerun$rerun[1:3])
#> $case1
#> lavaan 0.6.15 ended normally after 2 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                            99
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 6.557
#>   Degrees of freedom                                 2
#>   P-value (Chi-square)                           0.038
#> 
#> $case10
#> lavaan 0.6.15 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                            99
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 6.015
#>   Degrees of freedom                                 2
#>   P-value (Chi-square)                           0.049
#> 
#> $case100
#> lavaan 0.6.15 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                            99
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 6.620
#>   Degrees of freedom                                 2
#>   P-value (Chi-square)                           0.037

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)
fit_est_change
#> 
#> -- Standardized Case Influence on Parameter Estimates --
#> 
#>         m1~iv1 m1~iv2  dv~m1 m1~~m1 dv~~dv   gcd
#> case16   0.052 -0.038 -0.237 -0.004  0.624 0.450
#> case43  -0.403 -0.263 -0.135  0.223  0.120 0.302
#> case65   0.152  0.191  0.363  0.076  0.161 0.221
#> case85  -0.174  0.216 -0.119  0.335 -0.052 0.208
#> case51   0.421 -0.057  0.094  0.089 -0.044 0.200
#> case34  -0.314 -0.192 -0.109  0.189  0.030 0.178
#> case32  -0.247  0.195 -0.191  0.193  0.001 0.175
#> case18  -0.273  0.035  0.101  0.260 -0.046 0.156
#> case20  -0.239  0.204 -0.141  0.183 -0.032 0.156
#> case100 -0.001 -0.225 -0.069  0.305 -0.056 0.152
#> 
#> Note:
#> - Changes are standardized raw changes if a case is included.
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by generalized Cook's distance.
fit_est_change_paths_only <- est_change(fit_rerun,
                                parameters = c("m1 ~ iv1",
                                               "m1 ~ iv2",
                                               "dv ~ m1"))
fit_est_change_paths_only
#> 
#> -- Standardized Case Influence on Parameter Estimates --
#> 
#>        m1~iv1 m1~iv2  dv~m1   gcd
#> case43 -0.403 -0.263 -0.135 0.238
#> case51  0.421 -0.057  0.094 0.190
#> case65  0.152  0.191  0.363 0.189
#> case34 -0.314 -0.192 -0.109 0.142
#> case32 -0.247  0.195 -0.191 0.138
#> case20 -0.239  0.204 -0.141 0.121
#> case85 -0.174  0.216 -0.119 0.093
#> case11  0.010  0.149 -0.257 0.088
#> case18 -0.273  0.035  0.101 0.087
#> case13  0.274  0.059 -0.068 0.082
#> 
#> Note:
#> - Changes are standardized raw changes if a case is included.
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by generalized Cook's distance.

Raw Changes in Estimates

fit_est_change_raw <- est_change_raw(fit_rerun)
fit_est_change_raw
#> 
#> -- Case Influence on Parameter Estimates --
#> 
#>        id m1~iv1      id m1~iv2     id  dv~m1      id m1~~m1     id dv~~dv
#> 1  case51  0.046  case43 -0.026 case65  0.039  case61  0.043 case16  0.108
#> 2  case43 -0.043  case94  0.024 case11 -0.027  case85  0.041  case9  0.051
#> 3  case34 -0.033 case100 -0.022 case16 -0.024 case100  0.038 case76  0.050
#> 4  case13  0.030  case85  0.021 case32 -0.021  case18  0.032 case25  0.050
#> 5  case18 -0.029  case20  0.020 case99  0.020  case42  0.029 case91  0.043
#> 6  case32 -0.026  case32  0.019 case79  0.019  case43  0.028 case17  0.039
#> 7  case20 -0.025  case65  0.019 case93  0.018  case32  0.024 case65  0.030
#> 8  case75  0.021  case34 -0.019 case22  0.017  case34  0.024 case26  0.029
#> 9  case42 -0.020  case64 -0.017 case61 -0.017  case20  0.023 case62  0.027
#> 10 case68  0.020  case52  0.016 case25 -0.015  case40  0.023 case90  0.024
#> 
#> Note:
#> - Changes are raw changes if a case is included.
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by the absolute changes for each variable.

Mahalanobis Distance

fit_md <- mahalanobis_rerun(fit_rerun)
fit_md
#> 
#> -- Mahalanobis Distance --
#> 
#>            md
#> case16 11.530
#> case99 11.312
#> case87 11.091
#> case43 10.181
#> case51  9.869
#> case13  8.476
#> case91  8.078
#> case71  7.757
#> case17  7.555
#> case68  7.472
#> 
#> Note:
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by Mahalanobis distance in decreasing order.

Changes in Fit Measures

fit_mc <- fit_measures_change(fit_rerun,
            fit_measures = c("chisq", "cfi", "tli", "rmsea"))
fit_mc
#> 
#> -- Case Influence on Fit Measures --
#> 
#>          chisq    cfi    tli  rmsea
#> case1    0.154 -0.002 -0.005  0.002
#> case10   0.697 -0.013 -0.033  0.011
#> case100  0.092 -0.006 -0.015  0.001
#> case11   0.083 -0.002 -0.005  0.001
#> case12   0.173 -0.003 -0.007  0.002
#> case13  -0.909  0.020  0.050 -0.015
#> case14  -0.239  0.004  0.011 -0.005
#> case15   0.047  0.000  0.000  0.000
#> case16  -1.533  0.019  0.048 -0.024
#> case17  -1.591  0.027  0.066 -0.025
#> 
#> Note:
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.

All-In-One-Function

fit_influence <- influence_stat(fit_rerun)
fit_influence
#> 
#> -- Standardized Case Influence on Parameter Estimates --
#> 
#>         m1~iv1 m1~iv2  dv~m1 m1~~m1 dv~~dv   gcd
#> case16   0.052 -0.038 -0.237 -0.004  0.624 0.450
#> case43  -0.403 -0.263 -0.135  0.223  0.120 0.302
#> case65   0.152  0.191  0.363  0.076  0.161 0.221
#> case85  -0.174  0.216 -0.119  0.335 -0.052 0.208
#> case51   0.421 -0.057  0.094  0.089 -0.044 0.200
#> case34  -0.314 -0.192 -0.109  0.189  0.030 0.178
#> case32  -0.247  0.195 -0.191  0.193  0.001 0.175
#> case18  -0.273  0.035  0.101  0.260 -0.046 0.156
#> case20  -0.239  0.204 -0.141  0.183 -0.032 0.156
#> case100 -0.001 -0.225 -0.069  0.305 -0.056 0.152
#> 
#> Note:
#> - Changes are standardized raw changes if a case is included.
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by generalized Cook's distance.
#> 
#> -- Case Influence on Fit Measures --
#> 
#>          chisq    cfi  rmsea    tli
#> case1    0.154 -0.002  0.002 -0.005
#> case10   0.697 -0.013  0.011 -0.033
#> case100  0.092 -0.006  0.001 -0.015
#> case11   0.083 -0.002  0.001 -0.005
#> case12   0.173 -0.003  0.002 -0.007
#> case13  -0.909  0.020 -0.015  0.050
#> case14  -0.239  0.004 -0.005  0.011
#> case15   0.047  0.000  0.000  0.000
#> case16  -1.533  0.019 -0.024  0.048
#> case17  -1.591  0.027 -0.025  0.066
#> 
#> Note:
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> 
#> -- Mahalanobis Distance --
#> 
#>            md
#> case16 11.530
#> case99 11.312
#> case87 11.091
#> case43 10.181
#> case51  9.869
#> case13  8.476
#> case91  8.078
#> case71  7.757
#> case17  7.555
#> case68  7.472
#> 
#> Note:
#> - Only the first 10 case(s) is/are displayed. Set 'first' to NULL to display all cases.
#> - Cases sorted by Mahalanobis distance in decreasing order.

Diagnostic Plots

Generalized Cook’s Distance

gcd_plot(fit_influence, largest_gcd = 3)

Mahalanobis Distance

md_plot(fit_influence,
        largest_md = 3)

Fit Measure vs. Generalized Cook’s Distance

gcd_gof_plot(fit_influence,
             fit_measure = "rmsea",
             largest_gcd = 3,
             largest_fit_measure = 3)

Bubble Plot

gcd_gof_md_plot(fit_influence,
                fit_measure = "rmsea",
                largest_gcd = 3,
                largest_fit_measure = 3,
                largest_md = 3,
                circle_size = 15)

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.