Test Dataset: 4-Factor-3-Item
data_test_4_factor_3_item.RdA dataset for testing.
Format
A data frame with 500 rows and 4 variables:
- x1
Numeric.
- x2
Numeric.
- x3
Numeric.
- m1
Numeric.
- m2
Numeric.
- m3
Numeric.
- m4
Numeric.
- m5
Numeric.
- m6
Numeric.
- y1
Numeric.
- y2
Numeric.
- y3
Numeric.
Examples
library(lavaan)
data(data_test_4_factor_3_item)
mod <-
"
fx =~ x1 + x2 + x3
fm1 =~ m1 + m2 + m3
fm2 =~ m4 + m5 + m6
fy =~ y1 + y2 + y3
"
fit <- sem(mod, data_test_4_factor_3_item)
parameterEstimates(fit)
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 fx =~ x1 1.000 0.000 NA NA 1.000 1.000
#> 2 fx =~ x2 0.657 0.089 7.355 0.000 0.482 0.832
#> 3 fx =~ x3 0.638 0.087 7.305 0.000 0.467 0.809
#> 4 fm1 =~ m1 1.000 0.000 NA NA 1.000 1.000
#> 5 fm1 =~ m2 0.660 0.085 7.756 0.000 0.493 0.827
#> 6 fm1 =~ m3 0.825 0.105 7.873 0.000 0.619 1.030
#> 7 fm2 =~ m4 1.000 0.000 NA NA 1.000 1.000
#> 8 fm2 =~ m5 0.634 0.075 8.460 0.000 0.487 0.781
#> 9 fm2 =~ m6 0.609 0.073 8.352 0.000 0.466 0.752
#> 10 fy =~ y1 1.000 0.000 NA NA 1.000 1.000
#> 11 fy =~ y2 0.552 0.072 7.650 0.000 0.411 0.694
#> 12 fy =~ y3 0.639 0.081 7.861 0.000 0.480 0.798
#> 13 x1 ~~ x1 0.861 0.129 6.679 0.000 0.609 1.114
#> 14 x2 ~~ x2 1.046 0.085 12.249 0.000 0.879 1.213
#> 15 x3 ~~ x3 1.048 0.084 12.492 0.000 0.884 1.213
#> 16 m1 ~~ m1 0.963 0.126 7.669 0.000 0.717 1.209
#> 17 m2 ~~ m2 0.960 0.079 12.231 0.000 0.806 1.114
#> 18 m3 ~~ m3 1.074 0.102 10.547 0.000 0.874 1.273
#> 19 m4 ~~ m4 0.863 0.137 6.312 0.000 0.595 1.131
#> 20 m5 ~~ m5 1.017 0.084 12.152 0.000 0.853 1.182
#> 21 m6 ~~ m6 1.039 0.083 12.574 0.000 0.877 1.201
#> 22 y1 ~~ y1 0.946 0.162 5.844 0.000 0.629 1.264
#> 23 y2 ~~ y2 1.044 0.082 12.806 0.000 0.885 1.204
#> 24 y3 ~~ y3 0.950 0.087 10.953 0.000 0.780 1.120
#> 25 fx ~~ fx 0.965 0.155 6.225 0.000 0.661 1.269
#> 26 fm1 ~~ fm1 0.935 0.151 6.196 0.000 0.639 1.230
#> 27 fm2 ~~ fm2 1.226 0.174 7.055 0.000 0.885 1.567
#> 28 fy ~~ fy 1.298 0.198 6.554 0.000 0.910 1.686
#> 29 fx ~~ fm1 0.286 0.069 4.137 0.000 0.150 0.421
#> 30 fx ~~ fm2 0.421 0.079 5.342 0.000 0.267 0.576
#> 31 fx ~~ fy 0.127 0.075 1.704 0.088 -0.019 0.274
#> 32 fm1 ~~ fm2 0.205 0.072 2.836 0.005 0.063 0.346
#> 33 fm1 ~~ fy -0.048 0.072 -0.660 0.509 -0.190 0.094
#> 34 fm2 ~~ fy 0.352 0.085 4.143 0.000 0.186 0.519