lcsm (version 0.3.2)

sim_bi_lcsm: Simulate data from bivariate latent change score model parameter estimates

Description

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

Usage

sim_bi_lcsm(
  timepoints,
  model_x,
  model_x_param = NULL,
  model_y,
  model_y_param = NULL,
  coupling,
  coupling_param = NULL,
  sample.nobs = 500,
  na_x_pct = 0,
  na_y_pct = 0,
  seed = NULL,
  ...,
  var_x = "x",
  var_y = "y",
  change_letter_x = "g",
  change_letter_y = "j",
  return_lavaan_syntax = FALSE
)

Value

tibble

Arguments

timepoints

See specify_bi_lcsm

model_x

See specify_bi_lcsm

model_x_param

List, specifying parameter estimates for the LCSM that has been specified in the argument 'model_x':

• 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 change factor (g2).

• sigma2_g3: Variance of change factor (g3),

• sigma_g2lx1: Covariance of change factor (g2) with the initial true score x (lx1),

• sigma_g3lx1: Covariance of 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.

model_y

See specify_bi_lcsm

model_y_param

List, specifying parameter estimates for the LCSM that has been specified in the argument 'model_y':

• gamma_ly1: Mean of latent true scores y (Intercept),

• sigma2_ly1: Variance of latent true scores y,

• sigma2_uy: Variance of observed scores y,

• alpha_j2: Mean of change factor (j2),

• alpha_j3: Mean of change factor (j3),

• sigma2_j2: Variance of change factor (j2).

• sigma2_j3: Variance of change factor (j3),

• sigma_j2ly1: Covariance of change factor (j2) with the initial true score x (ly1),

• sigma_j3ly1: Covariance of change factor (j3) with the initial true score x (ly1),

• sigma_j2j3: Covariance of change factors (j2 and j2),

• phi_y: Autoregression of change scores y.

coupling

See specify_bi_lcsm

coupling_param

List, specifying parameter estimates coupling parameters that have been specified in the argument 'coupling':

• sigma_su: Covariance of residuals x and y,

• sigma_ly1lx1: Covariance of intercepts x and y,

• sigma_g2ly1: Covariance of change factor x (g2) with the initial true score y (ly1),

• sigma_g3ly1: Covariance of change factor x (g3) with the initial true score y (ly1),

• sigma_j2lx1: Covariance of change factor y (j2) with the initial true score x (lx1),

• sigma_j3lx1: Covariance of change factor y (j3) with the initial true score x (lx1),

• sigma_j2g2: Covariance of change factors y (j2) and x (g2),

• sigma_j2g3: Covariance of change factors y (j2) and x (g3),

• sigma_j3g2: Covariance of change factors y (j3) and x (g2),,

• delta_con_xy: Change score x (t) determined by true score y (t),

• delta_con_yx: Change score y (t) determined by true score x (t),

• delta_lag_xy: Change score x (t) determined by true score y (t-1),

• delta_lag_yx: Change score y (t) determined by true score x (t-1),

• xi_con_xy: Change score x (t) determined by change score y (t),

• xi_con_yx: Change score y (t) determined by change score x (t),

• xi_lag_xy: Change score x (t) determined by change score y (t-1),

• xi_lag_yx: Change score y (t) determined by change score x (t-1)

sample.nobs

Numeric, number of cases to be simulated, see specify_uni_lcsm

na_x_pct

Numeric, percentage of random missing values in the simulated dataset (0 to 1)

na_y_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

var_x

See specify_bi_lcsm

var_y

See specify_bi_lcsm

change_letter_x

See specify_bi_lcsm

change_letter_y

See specify_bi_lcsm

return_lavaan_syntax

Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat.

References

Ghisletta, P., & McArdle, J. J. (2012). Latent Curve Models and Latent Change Score Models Estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 651–682. tools:::Rd_expr_doi("10.1080/10705511.2012.713275").

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.

Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A.-L., de Mooij, S. M. M., Moutoussis, M., … Dolan, R. J. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117. tools:::Rd_expr_doi("10.1016/j.dcn.2017.11.007").

McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605. tools:::Rd_expr_doi("10.1146/annurev.psych.60.110707.163612").

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. tools:::Rd_expr_doi("10.18637/jss.v048.i02").

Examples

# Simulate data from bivariate LCSM parameters 
sim_bi_lcsm(timepoints = 12, 
            na_x_pct = .05,
            na_y_pct = .1,
            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 = FALSE)