Fit univariate latent change score models.

fit_uni_lcsm(
  data,
  var,
  model,
  add = NULL,
  mimic = "Mplus",
  estimator = "MLR",
  missing = "FIML",
  return_lavaan_syntax = FALSE,
  ...
)

Arguments

data

A data frame in "wide" format, i.e. one column for each measurement point and one row for each observation.

var

Vector, specifying the variable names of each measurement point sequentially.

model

List of model specifications (logical) for variables specified in var.

  • 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).

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:10.1080/10705511.2012.713275>.

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

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 univariate latent change score model fit_uni_lcsm(data = data_uni_lcsm, var = names(data_uni_lcsm)[2:4], model = list(alpha_constant = TRUE, beta = FALSE, phi = FALSE))
#> Warning: lavaan WARNING: some cases are empty and will be ignored: #> 179 223 239 258 306 359 430
#> lavaan 0.6-9 ended normally after 74 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 8 #> Number of equality constraints 2 #> #> Used Total #> Number of observations 493 500 #> Number of missing patterns 7 #> #> Model Test User Model: #> Standard Robust #> Test Statistic 0.703 0.704 #> Degrees of freedom 3 3 #> P-value (Chi-square) 0.873 0.872 #> Scaling correction factor 0.998 #> Yuan-Bentler correction (Mplus variant)