pcor

Maximum likelhood estimates of polychoric correlations

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

pcor function

<dl><dt>x</dt>
<dd>Variable 1</dd>
<dt>y</dt>
<dd>Variable 2</dd>
<dt>X</dt>
<dd>Optional covariates</dd>
<dt>start</dt>
<dd>Optional starting values</dd>
<dt>...</dt>
<dd>Additional arguments to lower level functions</dd></dl>

Arguments

Polychoric correlation — pcor

<dl>

<dt>x</dt>
<dd>Variable 1</dd>


<dt>y</dt>
<dd>Variable 2</dd>


<dt>X</dt>
<dd>Optional covariates</dd>


<dt>start</dt>
<dd>Optional starting values</dd>


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

</dl>

pcor: Polychoric correlation

Description

Usage

Arguments