IC.default

var_ic

Extract i.i.d. decomposition (influence function) from model object

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

IC function

<dl><dt>x</dt>
<dd>model object</dd>
<dt>...</dt>
<dd>additional arguments</dd>
<dt>id</dt>
<dd>(optional) id/cluster variable</dd>
<dt>bread</dt>
<dd>(optional) Inverse of derivative of mean score function</dd>
<dt>folds</dt>
<dd>(optional) Calculate aggregated iid decomposition (0:=disabled)</dd>
<dt>maxsize</dt>
<dd>(optional) Data is split in groups of size up to 'maxsize' (0:=disabled)</dd></dl>

Arguments

Extract i.i.d. decomposition (influence function) from model object — IC

<dl>

<dt>x</dt>
<dd>model object</dd>


<dt>...</dt>
<dd>additional arguments</dd>


<dt>id</dt>
<dd>(optional) id/cluster variable</dd>


<dt>bread</dt>
<dd>(optional) Inverse of derivative of mean score function</dd>


<dt>folds</dt>
<dd>(optional) Calculate aggregated iid decomposition (0:=disabled)</dd>


<dt>maxsize</dt>
<dd>(optional) Data is split in groups of size up to 'maxsize' (0:=disabled)</dd>

</dl>

IC: Extract i.i.d. decomposition (influence function) from model object

Description

Usage

Arguments

Examples