Compute Power Analysis For SEM Model Using Monte Carlo Simulation
Source:R/simulate_power.R
simulate_power.RdCompute Power Analysis For SEM Model Using Monte Carlo Simulation
Arguments
- model.population
The lavaan model with population estimate values specified.
- model
The lavaan model that will be tested in each simulation
- ksim
How many simulations the function should perform
- nobs
How many observations to generate in each simulation
Details
The function uses the simulate_data function from the lavaan package to perform a monte carlo simulation. The mean, std error, z value, p value and confidence intervals are then computed and reported in a table for each parameter.
Examples
library(lavaan)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
modpop <- '
M ~ 0.40*X
Y ~ 0.30*M
'
mod <- '
M ~ X
Y ~ M
'
simulate_power(modpop, mod)
#> lhs op rhs est se z pvalue ci.lower ci.upper Parameter
#> 1 M ~ X 0.310 0.099 3.132 0.002 0.116 0.504 M ~ X
#> 2 Y ~ M 0.318 0.094 3.382 0.001 0.134 0.502 Y ~ M
#> 3 M ~~ M 0.963 0.136 7.071 0.000 0.696 1.230 M ~~ M
#> 4 Y ~~ Y 0.933 0.132 7.071 0.000 0.675 1.192 Y ~~ Y
#> 5 X ~~ X 0.982 0.000 NA NA 0.982 0.982 X ~~ X
#> 6 M ~ X 0.263 0.111 2.384 0.017 0.047 0.480 M ~ X
#> 7 Y ~ M 0.440 0.095 4.649 0.000 0.254 0.625 Y ~ M
#> 8 M ~~ M 1.032 0.146 7.071 0.000 0.746 1.318 M ~~ M
#> 9 Y ~~ Y 0.975 0.138 7.071 0.000 0.705 1.245 Y ~~ Y
#> 10 X ~~ X 0.845 0.000 NA NA 0.845 0.845 X ~~ X
#> 11 M ~ X 0.375 0.096 3.885 0.000 0.186 0.564 M ~ X
#> 12 Y ~ M 0.301 0.096 3.135 0.002 0.113 0.489 Y ~ M
#> 13 M ~~ M 1.000 0.141 7.071 0.000 0.723 1.277 M ~~ M
#> 14 Y ~~ Y 1.060 0.150 7.071 0.000 0.767 1.354 Y ~~ Y
#> 15 X ~~ X 1.075 0.000 NA NA 1.075 1.075 X ~~ X
#> 16 M ~ X 0.543 0.094 5.753 0.000 0.358 0.728 M ~ X
#> 17 Y ~ M 0.321 0.100 3.226 0.001 0.126 0.516 Y ~ M
#> 18 M ~~ M 0.841 0.119 7.071 0.000 0.608 1.074 M ~~ M
#> 19 Y ~~ Y 1.109 0.157 7.071 0.000 0.802 1.417 Y ~~ Y
#> 20 X ~~ X 0.944 0.000 NA NA 0.944 0.944 X ~~ X
#> 21 M ~ X 0.448 0.095 4.693 0.000 0.261 0.635 M ~ X
#> 22 Y ~ M 0.425 0.095 4.488 0.000 0.240 0.611 Y ~ M
#> 23 M ~~ M 0.859 0.122 7.071 0.000 0.621 1.097 M ~~ M
#> 24 Y ~~ Y 0.941 0.133 7.071 0.000 0.680 1.202 Y ~~ Y
#> 25 X ~~ X 0.944 0.000 NA NA 0.944 0.944 X ~~ X
#> 26 M ~ X 0.434 0.086 5.027 0.000 0.265 0.603 M ~ X
#> 27 Y ~ M 0.343 0.100 3.444 0.001 0.148 0.539 Y ~ M
#> 28 M ~~ M 0.921 0.130 7.071 0.000 0.666 1.177 M ~~ M
#> 29 Y ~~ Y 1.146 0.162 7.071 0.000 0.828 1.464 Y ~~ Y
#> 30 X ~~ X 1.236 0.000 NA NA 1.236 1.236 X ~~ X
#> 31 M ~ X 0.668 0.091 7.364 0.000 0.490 0.846 M ~ X
#> 32 Y ~ M 0.307 0.080 3.829 0.000 0.150 0.464 Y ~ M
#> 33 M ~~ M 0.818 0.116 7.071 0.000 0.591 1.044 M ~~ M
#> 34 Y ~~ Y 0.811 0.115 7.071 0.000 0.586 1.035 Y ~~ Y
#> 35 X ~~ X 0.993 0.000 NA NA 0.993 0.993 X ~~ X
#> 36 M ~ X 0.346 0.110 3.141 0.002 0.130 0.561 M ~ X
#> 37 Y ~ M 0.240 0.089 2.697 0.007 0.065 0.414 Y ~ M
#> 38 M ~~ M 1.051 0.149 7.071 0.000 0.760 1.342 M ~~ M
#> 39 Y ~~ Y 0.911 0.129 7.071 0.000 0.659 1.164 Y ~~ Y
#> 40 X ~~ X 0.868 0.000 NA NA 0.868 0.868 X ~~ X
#> 41 M ~ X 0.440 0.096 4.600 0.000 0.253 0.628 M ~ X
#> 42 Y ~ M 0.280 0.101 2.775 0.006 0.082 0.477 Y ~ M
#> 43 M ~~ M 0.868 0.123 7.071 0.000 0.627 1.109 M ~~ M
#> 44 Y ~~ Y 1.069 0.151 7.071 0.000 0.773 1.365 Y ~~ Y
#> 45 X ~~ X 0.948 0.000 NA NA 0.948 0.948 X ~~ X
#> 46 M ~ X 0.286 0.104 2.741 0.006 0.081 0.490 M ~ X
#> 47 Y ~ M 0.343 0.099 3.448 0.001 0.148 0.537 Y ~ M
#> 48 M ~~ M 1.131 0.160 7.071 0.000 0.817 1.444 M ~~ M
#> 49 Y ~~ Y 1.201 0.170 7.071 0.000 0.868 1.533 Y ~~ Y
#> 50 X ~~ X 1.041 0.000 NA NA 1.041 1.041 X ~~ X