A six-variable dataset with 100 cases, with one influential case.
Format
A data frame with 100 rows and 7 variables:
- case_id
Case ID. Character.
- x1
Indicator. Numeric.
- x2
Indicator. Numeric.
- x3
Indicator. Numeric.
- x4
Indicator. Numeric.
- x5
Indicator. Numeric.
- x6
Indicator. Numeric.
Examples
library(lavaan)
data(cfa_dat2)
mod <-
"
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
"
fit <- cfa(mod, cfa_dat2)
summary(fit)
#> lavaan 0.6.17 ended normally after 36 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 13
#>
#> Number of observations 100
#>
#> Model Test User Model:
#>
#> Test statistic 16.218
#> Degrees of freedom 8
#> P-value (Chi-square) 0.039
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> f1 =~
#> x1 1.000
#> x2 2.875 1.336 2.152 0.031
#> x3 1.827 0.847 2.158 0.031
#> f2 =~
#> x4 1.000
#> x5 2.434 0.672 3.625 0.000
#> x6 1.940 0.523 3.709 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> f1 ~~
#> f2 0.063 0.035 1.797 0.072
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.919 0.134 6.840 0.000
#> .x2 0.383 0.162 2.366 0.018
#> .x3 0.542 0.100 5.438 0.000
#> .x4 0.767 0.113 6.793 0.000
#> .x5 0.227 0.136 1.675 0.094
#> .x6 0.472 0.108 4.378 0.000
#> f1 0.073 0.063 1.161 0.245
#> f2 0.149 0.078 1.926 0.054
#>
inf_out <- influence_stat(fit)
gcd_plot(inf_out)
