A nine-variable dataset with 200 cases.
Format
A data frame with 200 rows and 9 variables:
- x1
Indicator. Numeric.
- x2
Indicator. Numeric.
- x3
Indicator. Numeric.
- x4
Indicator. Numeric.
- x5
Indicator. Numeric.
- x6
Indicator. Numeric.
- x7
Indicator. Numeric.
- x8
Indicator. Numeric.
- x9
Indicator. Numeric.
Examples
library(lavaan)
data(sem_dat)
mod <-
"
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3 =~ x7 + x8 + x9
f2 ~ a * f1
f3 ~ b * f2
ab := a * b
"
fit <- sem(mod, sem_dat)
summary(fit)
#> lavaan 0.6.17 ended normally after 37 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 20
#>
#> Number of observations 200
#>
#> Model Test User Model:
#>
#> Test statistic 41.768
#> Degrees of freedom 25
#> P-value (Chi-square) 0.019
#>
#> 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 0.590 0.145 4.054 0.000
#> x3 0.808 0.168 4.812 0.000
#> f2 =~
#> x4 1.000
#> x5 0.730 0.099 7.400 0.000
#> x6 0.429 0.083 5.166 0.000
#> f3 =~
#> x7 1.000
#> x8 2.019 0.589 3.426 0.001
#> x9 2.747 0.788 3.486 0.000
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> f2 ~
#> f1 (a) 1.115 0.233 4.788 0.000
#> f3 ~
#> f2 (b) 0.206 0.061 3.394 0.001
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 1.183 0.173 6.831 0.000
#> .x2 1.129 0.127 8.909 0.000
#> .x3 1.027 0.134 7.667 0.000
#> .x4 0.833 0.173 4.812 0.000
#> .x5 1.078 0.140 7.714 0.000
#> .x6 1.234 0.132 9.367 0.000
#> .x7 1.056 0.112 9.428 0.000
#> .x8 1.042 0.139 7.478 0.000
#> .x9 1.077 0.197 5.470 0.000
#> f1 0.658 0.190 3.474 0.001
#> .f2 0.647 0.215 3.010 0.003
#> .f3 0.062 0.035 1.771 0.077
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|)
#> ab 0.230 0.079 2.895 0.004
#>