simulate_moderated_nonlinear_factor_analysis
Source:R/simulate_data.R
simulate_moderated_nonlinear_factor_analysis.Rdsimulate data for a moderated nonlinear factor analysis.
Value
data set with variables x1-x3 and y1-y3 representing repeated measurements of an affect measure. It is assumed that the autoregressive effect is different depending on covariate k
Examples
library(mxsem)
set.seed(123)
dataset <- simulate_moderated_nonlinear_factor_analysis(N = 2000)
model <- "
xi =~ x1 + x2 + x3
eta =~ y1 + y2 + y3
eta ~ a*xi
# we need two new parameters: a0 and a1. These are created as follows:
!a0
!a1
# Now, we redefine a to be a0 + k*a1, where k is found in the data
a := a0 + data.k*a1
"
mod <- mxsem(model = model,
data = dataset) |>
mxTryHard()
#> Running untitled32 with 20 parameters
#>
#> Beginning initial fit attempt
#> Running untitled32 with 20 parameters
#>
#> Lowest minimum so far: 9354.67567511442
#>
#> Solution found
#>
#>
#> Solution found! Final fit=9354.6757 (started at 63250.836) (1 attempt(s): 1 valid, 0 errors)
#> Start values from best fit:
#> 0.798644439069186,0.907904041125705,0.803838161335694,0.90039637675525,0.0423203089534444,0.0419257504471055,0.0370993727609707,0.0410548523456912,0.0419748898902563,0.0377187198122035,0.983487233943373,0.248565811440238,0.0132158323116108,0.00300763175648384,0.0101763456547476,-0.0043236584301499,-0.000604331921656022,-0.00278126962209998,0.679954707463475,-0.17245961832583
omxGetParameters(mod)
#> xi→x2 xi→x3 eta→y2 eta→y3 x1↔x1
#> 0.7986444391 0.9079040411 0.8038381613 0.9003963768 0.0423203090
#> x2↔x2 x3↔x3 y1↔y1 y2↔y2 y3↔y3
#> 0.0419257504 0.0370993728 0.0410548523 0.0419748899 0.0377187198
#> xi↔xi eta↔eta one→x1 one→x2 one→x3
#> 0.9834872339 0.2485658114 0.0132158323 0.0030076318 0.0101763457
#> one→y1 one→y2 one→y3 a0 a1
#> -0.0043236584 -0.0006043319 -0.0027812696 0.6799547075 -0.1724596183