sim_data

Simulate data from a multivariate normal distribution.

The Directed Prediction Index ('DPI') is
a quasi-causal inference (causal discovery) method for observational data
designed to quantify the relative endogeneity (relative dependence)
of outcome (Y) versus predictor (X) variables in regression models.
By comparing the proportion of variance explained (R-squared)
between the Y-as-outcome model and the X-as-outcome model
while controlling for a sufficient number of possible confounders,
it can suggest a plausible (admissible) direction of influence
from a less endogenous variable (X) to a more endogenous variable (Y).
Methodological details are provided at
<https://psychbruce.github.io/DPI/>.
This package also includes functions for data simulation and network
analysis (correlation, partial correlation, and Bayesian networks).

Han-Wu-Shuang Bao

The Directed Prediction Index for Causal Direction Inference
from Observational Data

sim_data function

<dl><dt>n</dt>
<dd>Number of observations (cases).</dd>
<dt>k</dt>
<dd>Number of variables. Will be ignored if <code>cor</code> specifies a correlation matrix.</dd>
<dt>cor</dt>
<dd>A correlation value or correlation matrix of the variables. Defaults to <code>NULL</code> that generates completely random data regardless of their empirical correlations.</dd>
<dt>exact</dt>
<dd>Ensure the sample correlation matrix to be exact as specified in <code>cor</code>. This argument is passed on to <code>empirical</code> in <code><a href="/link/mvrnorm()?package=DPI&version=2025.11" data-mini-rdoc="DPI::mvrnorm()">mvrnorm()</a></code>. Defaults to <code>TRUE</code>.</dd>
<dt>seed</dt>
<dd>Random seed for replicable results. Defaults to <code>NULL</code>.</dd></dl>

Arguments

Simulate data from a multivariate normal distribution. — sim_data

<dl>

<dt>n</dt>
<dd>Number of observations (cases).</dd>


<dt>k</dt>
<dd>Number of variables. Will be ignored if <code>cor</code> specifies a correlation matrix.</dd>


<dt>cor</dt>
<dd>A correlation value or correlation matrix of the variables. Defaults to <code>NULL</code> that generates completely random data regardless of their empirical correlations.</dd>


<dt>exact</dt>
<dd>Ensure the sample correlation matrix to be exact as specified in <code>cor</code>. This argument is passed on to <code>empirical</code> in <code><a href='https://rdrr.io/pkg/MASS/man/mvrnorm.html'>mvrnorm()</a></code>. Defaults to <code>TRUE</code>.</dd>


<dt>seed</dt>
<dd>Random seed for replicable results. Defaults to <code>NULL</code>.</dd>

</dl>

sim_data: Simulate data from a multivariate normal distribution.

Description

Usage

Value

Arguments

See Also

Examples