ggs_pcp

Plot a histogram with the distribution of correctly predicted cases in a model against a binary response variable.

Tools for assessing and diagnosing convergence of
Markov Chain Monte Carlo simulations, as well as for graphically display
results from full MCMC analysis. The package also facilitates the graphical
interpretation of models by providing flexible functions to plot the
results against observed variables, and functions to work with
hierarchical/multilevel batches of parameters
(Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).

Xavier i Marín

ggmcmc

Tools for Analyzing MCMC Simulations from Bayesian Inference

Xavier Fernández i Marín

ggs_pcp function

<dl><dt>D</dt>
<dd>Data frame whith the simulations. Notice that only the fitted / expected posterior outcomes are needed, and so either the previous call to ggs() should have limited the family of parameters to only pass the fitted / expected values. See the example below.</dd>
<dt>outcome</dt>
<dd>vector (or matrix or array) containing the observed outcome variable. Currently only a vector is supported.</dd>
<dt>threshold</dt>
<dd>numerical bounded between 0 and 1 or "observed", the default. If "observed", the threshold of expected values to be considered a realization of the event (1, succes) is computed using the observed value in the data. Otherwise, a numerical value showing which threshold to use (typically, 0.5) can be given.</dd>
<dt>bins</dt>
<dd>integer indicating the total number of bins in which to divide the histogram. Defaults to 30, which is the same as geom_histogram()</dd></dl>

Arguments

Plot for model fit of binary response variables: percent correctly predicted — ggs_pcp

<dl>

<dt>D</dt>
<dd>Data frame whith the simulations. Notice that only the fitted / expected posterior outcomes are needed, and so either the previous call to ggs() should have limited the family of parameters to only pass the fitted / expected values. See the example below.</dd>


<dt>outcome</dt>
<dd>vector (or matrix or array) containing the observed outcome variable. Currently only a vector is supported.</dd>


<dt>threshold</dt>
<dd>numerical bounded between 0 and 1 or "observed", the default. If "observed", the threshold of expected values to be considered a realization of the event (1, succes) is computed using the observed value in the data. Otherwise, a numerical value showing which threshold to use (typically, 0.5) can be given.</dd>


<dt>bins</dt>
<dd>integer indicating the total number of bins in which to divide the histogram. Defaults to 30, which is the same as geom_histogram()</dd>

</dl>

ggs_pcp: Plot for model fit of binary response variables: percent correctly predicted

Description

Usage

Value

Arguments

Examples