p_to_bf

Convert p values to approximate (pseudo) Bayes Factors (PseudoBF10). This transformation has been suggested by Wagenmakers (2022).

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

p_to_bf function

<dl><dt>p</dt>
<dd>p value(s).</dd>
<dt>n</dt>
<dd>Number of observations.</dd>
<dt>log</dt>
<dd>Return <code>log(BF10)</code> or raw <code>BF10</code>. Defaults to <code>FALSE</code>.</dd>
<dt>label</dt>
<dd>Add labels (i.e., names) to returned values. Defaults to <code>FALSE</code>.</dd></dl>

Arguments

Convert p values to approximate (pseudo) Bayes Factors (PseudoBF10). — p_to_bf

<dl>

<dt>p</dt>
<dd>p value(s).</dd>


<dt>n</dt>
<dd>Number of observations.</dd>


<dt>log</dt>
<dd>Return <code>log(BF10)</code> or raw <code>BF10</code>. Defaults to <code>FALSE</code>.</dd>


<dt>label</dt>
<dd>Add labels (i.e., names) to returned values. Defaults to <code>FALSE</code>.</dd>

</dl>

p_to_bf: Convert p values to approximate (pseudo) Bayes Factors (PseudoBF10).

Description

Usage

Value

Arguments

References

See Also

Examples