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Fit bivariate latent change score models

Source: R/fit_lcsm.R
fit_bi_lcsm.Rd

Fit bivariate latent change score models.

Usage

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. In an example with 10 repeated measurements, setting alpha_piecewise_num to 5 would estimate two seperate constant change factors, a first one for changes up to timepoint 5, and a second one for changes from timepoint 5 onwards (in this example timepoint 10).,

  • 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. In an example with 10 repeated measurements, setting alpha_piecewise_num to 5 would estimate two seperate constant change factors, a first one for changes up to timepoint 5, and a second one for changes from timepoint 5 onwards (in this example timepoint 10).,

  • 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. doi:10.18637/jss.v048.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
#>     (= 1.264160e-15) is close to zero. This may be a symptom that the
#>     model is not identified.
#> lavaan 0.6.14 ended normally after 137 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      Scaled
#>   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)