sim_uni_lcsm: Simulate data from univariate latent change score model...

In lcsm: Univariate and Bivariate Latent Change Score Modelling

Simulate data from univariate latent change score model parameter estimates

Description

This function simulate data from univariate latent change score model parameter estimates using simulateData.

Usage

sim_uni_lcsm(
  timepoints,
  model,
  model_param = NULL,
  var = "x",
  change_letter = "g",
  sample.nobs = 500,
  na_pct = 0,
  seed = NULL,
  ...,
  return_lavaan_syntax = FALSE
)

Arguments

timepoints	See specify_uni_lcsm
model	See specify_uni_lcsm
model_param	List, specifying parameter estimates for the LCSM that has been specified in the argument 'model' gamma_lx1: Mean of latent true scores x (Intercept), sigma2_lx1: Variance of latent true scores x, sigma2_ux: Variance of observed scores x, alpha_g2: Mean of change factor (g2), alpha_g3: Mean of change factor (g3), sigma2_g2: Variance of constant change factor (g2). sigma2_g3: Variance of constant change factor (g3), sigma_g2lx1: Covariance of constant change factor (g2) with the initial true score x (lx1), sigma_g3lx1: Covariance of constant change factor (g3) with the initial true score x (lx1), sigma_g2g3: Covariance of change factors (g2 and g2), phi_x: Autoregression of change scores x.
var	See specify_uni_lcsm
change_letter	See specify_uni_lcsm
sample.nobs	Numeric, number of cases to be simulated, see specify_uni_lcsm
na_pct	Numeric, percentage of random missing values in the simulated dataset (0 to 1)
seed	Set seed for data simulation, see simulateData
...	Arguments to be passed on to simulateData
return_lavaan_syntax	Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat.

Value

tibble

Examples

# Simulate data from univariate LCSM parameters 
sim_uni_lcsm(timepoints = 10, 
             model = list(alpha_constant = TRUE, beta = FALSE, phi = TRUE), 
             model_param = list(gamma_lx1 = 21, 
                                sigma2_lx1 = 1.5,
                                sigma2_ux = .2, 
                                alpha_g2 = -.93,
                                sigma2_g2 = .1,
                                sigma_g2lx1 = .2,
                                phi_x = .2),
             return_lavaan_syntax = FALSE, 
             sample.nobs = 1000,
             na_pct = .3)

lcsm documentation built on March 7, 2023, 6:45 p.m.