Simulate data from bivariate latent change score model parameter estimates
Source:R/sim_lcsm_data.R
sim_bi_lcsm.RdThis 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
)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. 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. 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. 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. 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)
#> Parameter estimates for the data simulation are taken from the argument 'model_param'.
#> All parameter estimates for the LCSM have been specified in the argument 'model_param'.
#> # A tibble: 500 × 25
#> id x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 21.3 19.6 17.3 15.1 12.1 9.87 7.75 5.58 3.89 1.78 0.185
#> 2 2 19.9 17.2 14.4 12.1 10.3 7.21 4.69 1.84 0.174 -2.44 -3.25
#> 3 3 20.4 17.4 14.9 12.4 10.4 7.94 5.31 4.10 1.46 -1.14 -1.99
#> 4 4 20.8 20.7 19.4 18.7 17.1 15.3 14.2 12.8 10.9 9.96 8.43
#> 5 5 20.2 18.6 16.7 14.1 11.6 10.3 7.23 4.89 3.62 0.458 -0.172
#> 6 6 20.5 19.9 18.4 16.7 14.9 14.0 11.6 11.2 8.13 7.80 6.60
#> 7 7 20.6 18.6 17.8 16.6 14.9 13.4 NA 10.1 9.78 8.10 6.67
#> 8 8 23.1 21.7 21.7 19.7 19.8 18.9 18.7 17.3 16.2 15.2 15.2
#> 9 9 22.0 19.1 18.4 16.7 14.6 13.6 11.4 10.7 8.37 7.29 5.92
#> 10 10 20.5 18.4 15.5 12.8 10.9 8.23 6.65 3.97 NA -0.00326 -1.19
#> # … with 490 more rows, and 13 more variables: x12 <dbl>, y1 <dbl>, y2 <dbl>,
#> # y3 <dbl>, y4 <dbl>, y5 <dbl>, y6 <dbl>, y7 <dbl>, y8 <dbl>, y9 <dbl>,
#> # y10 <dbl>, y11 <dbl>, y12 <dbl>