dsep.lvm

dsep

Check for conditional independence (d-separation)

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

Brice Ozenne

Thomas Gerds

dsep.lvm function

<dl><dt>object</dt>
<dd>lvm object</dd>
<dt>x</dt>
<dd>Variables for which to check for conditional independence</dd>
<dt>cond</dt>
<dd>Conditioning set</dd>
<dt>return.graph</dt>
<dd>If TRUE the moralized ancestral graph with the
conditioning set removed is returned</dd>
<dt>...</dt>
<dd>Additional arguments to lower level functions</dd></dl>

Arguments

Check d-separation criterion — dsep.lvm

<dl>

<dt>object</dt>
<dd>lvm object</dd>


<dt>x</dt>
<dd>Variables for which to check for conditional independence</dd>


<dt>cond</dt>
<dd>Conditioning set</dd>


<dt>return.graph</dt>
<dd>If TRUE the moralized ancestral graph with the
conditioning set removed is returned</dd>


<dt>...</dt>
<dd>Additional arguments to lower level functions</dd>

</dl>

dsep.lvm: Check d-separation criterion

Description

Usage

Arguments

Details

Examples