makemissing

Generates missing entries in data.frame/matrix

utilities

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

makemissing function

<dl><dt>data</dt>
<dd>data.frame</dd>
<dt>p</dt>
<dd>Fraction of missing data in each column</dd>
<dt>cols</dt>
<dd>Which columns (name or index) to alter</dd>
<dt>rowwise</dt>
<dd>Should missing occur row-wise (either none or all selected columns are missing)</dd>
<dt>nafun</dt>
<dd>(Optional) function to be applied on data.frame before return (e.g. <code>na.omit</code> to return complete-cases only)</dd>
<dt>seed</dt>
<dd>Random seed</dd></dl>

Arguments

Author

Create random missing data — makemissing

<dl>

<dt>data</dt>
<dd>data.frame</dd>


<dt>p</dt>
<dd>Fraction of missing data in each column</dd>


<dt>cols</dt>
<dd>Which columns (name or index) to alter</dd>


<dt>rowwise</dt>
<dd>Should missing occur row-wise (either none or all selected columns are missing)</dd>


<dt>nafun</dt>
<dd>(Optional) function to be applied on data.frame before return (e.g. <code>na.omit</code> to return complete-cases only)</dd>


<dt>seed</dt>
<dd>Random seed</dd>

</dl>

makemissing: Create random missing data

Description

Usage

Value

Arguments

Author