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,
  ...
)

Arguments

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 var_x.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors),

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

model_y

List of model specifications for variables specified in var_y.

  • alpha_constant (Constant change factor),

  • alpha_piecewise (Piecewise constant change factors),

  • alpha_piecewise_num (Changepoint of piecewise constant change factors),

  • alpha_linear (Linear change factor),

  • beta (Proportional change factor),

  • phi (Autoregression of change scores).

coupling

List of model specifications (logical) for coupling parameters.

  • coupling_piecewise (Piecewise coupling parameters),

  • coupling_piecewise_num (Changepoint of piecewise coupling parameters),

  • delta_xy (True score y predicting subsequent change score x),

  • delta_yx (True score x predicting subsequent change score y),

  • xi_xy (Change score y predicting subsequent change score x),

  • xi_yx (Change score x predicting subsequent change score y).

add

String, lavaan syntax to be added to the model

mimic

See `mimic` argument in lavOptions.

estimator

See `estimator` argument in lavOptions.

missing

See `missing` argument in lavOptions.

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.

Value

This function returns a lavaan class object.

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:10.1146/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. http://www.jstatsoft.org/v48/i02/.

Examples

# 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) )
#> Warning: lavaan WARNING: #> The variance-covariance matrix of the estimated parameters (vcov) #> does not appear to be positive definite! The smallest eigenvalue #> (= 1.058210e-15) is close to zero. This may be a symptom that the #> model is not identified.
#> lavaan 0.6-9 ended normally after 130 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 31 #> Number of equality constraints 9 #> #> Number of observations 500 #> Number of missing patterns 23 #> #> Model Test User Model: #> Standard Robust #> Test Statistic 6.870 5.971 #> Degrees of freedom 5 5 #> P-value (Chi-square) 0.230 0.309 #> Scaling correction factor 1.151 #> Yuan-Bentler correction (Mplus variant)