var_ic

Computes the empirical variance of the influence function, providing a
sandwich-type variance estimator.

A general implementation of Structural Equation Models
with latent variables (MLE, 2SLS, and composite likelihood
estimators) with both continuous, censored, and ordinal
outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
Mixture latent variable models and non-linear latent variable models
(Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>).
The package also provides methods for graph exploration (d-separation,
back-door criterion), simulation of general non-linear latent variable
models, and estimation of influence functions for a broad range of
statistical models.

Klaus Holst

lava

Latent Variable Models

Klaus K. Holst

Benedikt Sommer

Brice Ozenne

Thomas Gerds

var_ic function

<dl><dt>x</dt>
<dd>Influence function matrix (observations x parameters), typically
obtained from IC.</dd>
<dt>...</dt>
<dd>Additional arguments (currently not used)</dd></dl>

Arguments

Variance based on influence function — var_ic

<dl>

<dt>x</dt>
<dd>Influence function matrix (observations x parameters), typically
obtained from IC.</dd>


<dt>...</dt>
<dd>Additional arguments (currently not used)</dd>

</dl>

var_ic: Variance based on influence function

Description

Usage

Value

Arguments