ggmGenerator

Generates data given a partial correlation network. Data can be made ordinal by using a threshold model with equally spaced thresholds.

Bootstrap methods to assess accuracy and stability of estimated network structures
and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible
specification of any undirected network estimation procedure in R, and offers
default sets for various estimation routines.

Sacha Epskamp

bootnet

Bootstrap Methods for Various Network Estimation Routines

Eiko I. Fried

ggmGenerator function

<dl> <dt>ordinal</dt>
<dd>Logical, should ordinal data be generated?</dd> <dt>nLevels</dt>
<dd>Number of levels used in ordinal data.</dd> <dt>skewFactor</dt>
<dd>How skewed should ordinal data be? 1 indicates uniform data and higher values increase skewedness.</dd> <dt>type</dt>
<dd>Should thresholds for ordinal data be sampled at random or determined uniformly?</dd> <dt>missing</dt>
<dd>Proportion of data that should be simulated to be missing.</dd></dl>

Arguments

Sacha Epskamp &lt;mail@sachaepskamp.com&gt;

Author

Generates a function that simulates data from the Gaussian graphical model (GGM) — ggmGenerator

<dl>

 <dt>ordinal</dt>
<dd>Logical, should ordinal data be generated?</dd>

 <dt>nLevels</dt>
<dd>Number of levels used in ordinal data.</dd>

 <dt>skewFactor</dt>
<dd>How skewed should ordinal data be? 1 indicates uniform data and higher values increase skewedness.</dd>

 <dt>type</dt>
<dd>Should thresholds for ordinal data be sampled at random or determined uniformly?</dd>

 <dt>missing</dt>
<dd>Proportion of data that should be simulated to be missing.</dd>

</dl>

ggmGenerator: Generates a function that simulates data from the Gaussian graphical model (GGM)

Description

Usage

Arguments

Author