In milanwiedemann/lcstools: Univariate and Bivariate Latent Change Score Modelling

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(lcsm)

The functions sim_uni_lcsm() and sim_bi_lcsm() simulate data based on some some parameters that can be specified. A full list of parameters that can be specified for the data simulation can be found in the README file on GitHub.

# Simulate some data 
sim_uni_lcsm(timepoints = 5, 
             model = list(alpha_constant = TRUE, beta = FALSE, phi = TRUE), 
             model_param = list(gamma_lx1 = 21, 
                                sigma2_lx1 = 1.5,
                                sigma2_ux = 0.2,
                                alpha_j2 = -0.93,
                                sigma2_j2 = 0.1,
                                sigma_j2lx1 = 0.2,
                                phi_x = 0.3),
             sample.nobs = 1000,
             na_pct = 0.3)

It is also possible to return the lavaan syntax instead of simulating data for further manual specifications. The modified object could then be used to simulate data using lavaan::simulateData().

# Return lavaan syntax based on the following argument specifications
simsyntax <- sim_bi_lcsm(timepoints = 5, 
                         model_x = list(alpha_constant = TRUE, beta = TRUE, phi = FALSE),
                         model_x_param = list(gamma_lx1 = 21,
                                              sigma2_lx1 = .5,
                                              sigma2_ux = .2,
                                              alpha_g2 = -.4,
                                              sigma2_g2 = .4,
                                              sigma_g2lx1 = .2,
                                              beta_x = -.1),
                         model_y = list(alpha_constant = TRUE, beta = TRUE, phi = TRUE),
                         model_y_param = list(gamma_ly1 = 5,
                                              sigma2_ly1 = .2,
                                              sigma2_uy = .2,
                                              alpha_j2 = -.2,
                                              sigma2_j2 = .1,
                                              sigma_j2ly1 = .02,
                                              beta_y = -.2,
                                              phi_y = .1),
                         coupling = list(delta_lag_xy = TRUE, 
                                         xi_lag_yx = TRUE),
                         coupling_param = list(sigma_su = .01,
                                               sigma_ly1lx1 = .2,
                                               sigma_g2ly1 = .1,
                                               sigma_j2lx1 = .1,
                                               sigma_j2g2 = .01,
                                               delta_lag_xy = .13,
                                               xi_lag_yx = .4),
                        return_lavaan_syntax = TRUE)

I'm using the function cat() here to make the output more readable. This has no effect on the information that is returned, it is just another way to format the syntax and lavaan knows how to read either format as long as it's a string, i.e. surrounded by quotation marks.