rnet.gnp

Call <code><a href="/link/igraph%3A%3Asample_gnp?package=simcausal&version=0.5.7" data-mini-rdoc="simcausal::igraph::sample_gnp">igraph::sample_gnp</a></code> and convert the output to <code>simcausal</code> network matrix.

A flexible tool for simulating complex longitudinal data using
structural equations, with emphasis on problems in causal inference.
Specify interventions and simulate from intervened data generating
distributions. Define and evaluate treatment-specific means, the average
treatment effects and coefficients from working marginal structural models.
User interface designed to facilitate the conduct of transparent and
reproducible simulation studies, and allows concise expression of complex
functional dependencies for a large number of time-varying nodes. See the
package vignette for more information, documentation and examples.

Fred Gruber

simcausal

Simulating Longitudinal Data with Causal Inference Applications

Oleg Sofrygin

Romain Neugebauer

rnet.gnp function

<dl><dt>n</dt>
<dd>Size of the network graph (number of nodes).</dd>
<dt>p</dt>
<dd>Same as <code><a href="/link/igraph%3A%3Asample_gnp?package=simcausal&version=0.5.7" data-mini-rdoc="simcausal::igraph::sample_gnp">igraph::sample_gnp</a></code>: The probability for drawing an edge between two arbitrary vertices (G(n,p) graph).</dd></dl>

Arguments

Call <code><a href='https://rdrr.io/pkg/igraph/man/sample_gnp.html'>igraph::sample_gnp</a></code> and convert the output to <code>simcausal</code> network matrix.

Call igraph::sample_gnp to Generate Random Graph Object According to the G(n,p) Erdos-Renyi Model — rnet.gnp

<dl>

<dt>n</dt>
<dd>Size of the network graph (number of nodes).</dd>


<dt>p</dt>
<dd>Same as <code><a href='https://rdrr.io/pkg/igraph/man/sample_gnp.html'>igraph::sample_gnp</a></code>: The probability for drawing an edge between two arbitrary vertices (G(n,p) graph).</dd>

</dl>

rnet.gnp: Call `igraph::sample_gnp` to Generate Random Graph Object According to the G(n,p) Erdos-Renyi Model

Description

Usage

Value

Arguments

See Also