The basic power analysis as described in the Vignette Getting started can be extended to using bounded estimation, estimation with constraints over time, and the inclusion of measurement error variances in the generated data and estimation model. These extensions are described below.

Bounded estimation with lavaan

To prevent non-convergence for small sample sizes (say, less than 100), bounds can be imposed on the parameter space during estimation of the model using bounds = TRUE (De Jonckere & Rosseel, 2022). This can aid the optimization algorithm to find unique solutions and prevents it from searching in the completely wrong direction for one, or multiple parameters. Sensible lower bounds involve those on the (residual) variances of latent variables (e.g., the random intercept variances), as negative variances are theoretically not possible. Upper bound for variances are determined based on the observed variances for variable. In the context of the RI-CLPM, the factor loadings are (usually) fixed, and hence these parameters are not estimated. The lagged effects are theoretically infinite, and hence there are no sensible bounds we can place à priori on these parameters.

Constraints over time

powRICLPM() offers users the option to impose various constraints over time on the estimation model through the constraints argument. This has statistical advantages as constraints over time reduce model complexity, thereby potentially reducing convergence issues and increasing power. Moreover, some researchers are interested in so called ‘stationarity’ constraints for theoretical reasons. A disadvantage of such constraints is that they assume certain parameters to be time-invariant. This might not be an assumption researchers are willing to make, especially in developmental contexts where you expect lagged effects might change over time (e.g., the variable wA gets more important in driving wB as one gets older). Therefore, by default constraints = "none", implying that all lagged effects, and within-components (residual) variances and covariances are freely estimated over time.

Constraint options include:

Measurement error

While it is generally advisable to include measurement error when analyzing psychological data, the RI-CLPM does not include it. Adding measurement error to the model would result in the bivariate stable trait autoregressive trait state (STARTS) model by Kenny and Zautra (2001), and requires at least 4 waves of data to be identified. Users can add measurement error variances to the estimation model by specifying estimate_ME = TRUE. Measurement error can be added to the simulated data using the reliability argument.

Note, however, that the STARTS model has been shown to be prone to empirical under-identification, often requiring upwards of 8 waves of data and sample sizes larger than 500.