estimate.array

estimate.data.frame

Estimate parameters for the sample mean, variance, and quantiles

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

estimate.array function

<dl><dt>x</dt>
<dd>numeric matrix</dd>
<dt>type</dt>
<dd>target parameter ("mean", "variance", "quantile"). The type of
quantile (see <code><a href="/link/stats%3A%3Aquantile?package=lava&version=1.9.3" data-mini-rdoc="lava::stats::quantile">stats::quantile</a></code>) can be chosen with "quantile1",
"quantile2", ...</dd>
<dt>probs</dt>
<dd>numeric vector of probabilities (for type="quantile")</dd>
<dt>...</dt>
<dd>Additional arguments to lower level functions (i.e.,
stats::density.default when type="quantile")</dd></dl>

Arguments

Estimate parameters and influence function. — estimate.array

<dl>

<dt>x</dt>
<dd>numeric matrix</dd>


<dt>type</dt>
<dd>target parameter ("mean", "variance", "quantile"). The type of
quantile (see <code><a href='https://rdrr.io/r/stats/quantile.html'>stats::quantile</a></code>) can be chosen with "quantile1",
"quantile2", ...</dd>


<dt>probs</dt>
<dd>numeric vector of probabilities (for type="quantile")</dd>


<dt>...</dt>
<dd>Additional arguments to lower level functions (i.e.,
stats::density.default when type="quantile")</dd>

</dl>

estimate.array: Estimate parameters and influence function.

Description

Usage

Value

Arguments