fit_bi_lcsm | R Documentation |
Fit bivariate latent change score models.
fit_bi_lcsm( data, var_x, var_y, model_x, model_y, coupling, add = NULL, mimic = "Mplus", estimator = "MLR", missing = "FIML", return_lavaan_syntax = FALSE, ... )
data |
Wide dataset. |
var_x |
List of variables measuring one construct of the model. |
var_y |
List of variables measuring another construct of the model. |
model_x |
List of model specifications (logical) for variables specified in
|
model_y |
List of model specifications for variables specified in
|
coupling |
List of model specifications (logical) for coupling parameters.
|
add |
String, lavaan syntax to be added to the model |
mimic |
See |
estimator |
See |
missing |
See |
return_lavaan_syntax |
Logical, if TRUE return the lavaan syntax used for simulating data. To make it look beautiful use the function cat. |
... |
Additional arguments to be passed to lavOptions. |
This function returns a lavaan class object.
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: 10.1146/annurev.psych.60.110707.163612.
Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling—Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.
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.
# Fit fit_bi_lcsm(data = data_bi_lcsm, var_x = names(data_bi_lcsm)[2:4], var_y = names(data_bi_lcsm)[12:14], model_x = list(alpha_constant = TRUE, beta = TRUE, phi = FALSE), model_y = list(alpha_constant = TRUE, beta = TRUE, phi = TRUE), coupling = list(delta_lag_xy = TRUE, xi_lag_yx = TRUE) )
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