.parse_formula.indicators

internal

In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.

Stephen Martin

LMMELSM

Fit Latent Multivariate Mixed Effects Location Scale Models

Philippe Rast

.parse_formula.indicators function

<dl><dt>mlist</dt>
<dd>List of measurement formulas.</dd>
<dt>mf</dt>
<dd>Data frame with complete cases.</dd></dl>

Arguments

Author

Compute indicator data. — .parse_formula.indicators

<dl>

<dt>mlist</dt>
<dd>List of measurement formulas.</dd>


<dt>mf</dt>
<dd>Data frame with complete cases.</dd>

</dl>

.parse_formula.indicators: Compute indicator data.

Description

Usage

Value

Arguments

Author