A six-variable dataset with 100 cases.
Format
A data frame with 100 rows and 6 variables:
- x1
Indicator. Numeric.
- x2
Indicator. Numeric.
- x3
Indicator. Numeric.
- x4
Indicator. Numeric.
- x5
Indicator. Numeric.
- x6
Indicator. Numeric.
Examples
library(lavaan)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
data(cfa_dat)
mod <-
"
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
"
fit <- cfa(mod, cfa_dat)
summary(fit)
#> lavaan 0.6.17 ended normally after 37 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 13
#>
#> Number of observations 100
#>
#> Model Test User Model:
#>
#> Test statistic 12.027
#> Degrees of freedom 8
#> P-value (Chi-square) 0.150
#>
#> 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 0.767 0.225 3.405 0.001
#> x3 1.047 0.296 3.542 0.000
#> f2 =~
#> x4 1.000
#> x5 2.114 0.869 2.431 0.015
#> x6 0.992 0.377 2.635 0.008
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> f1 ~~
#> f2 0.171 0.091 1.884 0.060
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.841 0.221 3.802 0.000
#> .x2 1.214 0.208 5.823 0.000
#> .x3 1.018 0.251 4.064 0.000
#> .x4 1.103 0.186 5.918 0.000
#> .x5 0.993 0.437 2.270 0.023
#> .x6 0.882 0.158 5.575 0.000
#> f1 0.708 0.262 2.703 0.007
#> f2 0.250 0.151 1.659 0.097
#>