UScrime_data

The dataset has been borrowed from the MASS R package and describes 
the effect of punishment regimes on crime rates. One explanatory
variable (indicator variable for a Southern state) was removed since it was binary.

Performs Bayesian variable selection under normal linear
models for the data with the model parameters following as prior distributions either
the power-expected-posterior (PEP) or the intrinsic (a special case of the former)
(Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>,
Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>).
The prior distribution on model space is the uniform over all models
or the uniform on model dimension (a special case of the beta-binomial prior).
The selection is performed by either implementing a full enumeration
and evaluation of all possible models or using the Markov Chain
Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>).
Complementary functions for hypothesis testing, estimation and
predictions under Bayesian model averaging, as well as, plotting and
printing the results are also provided. The results can be compared to the
ones obtained under other well-known priors on model parameters and model spaces.

Konstantina Charmpi

PEPBVS

Bayesian Variable Selection using Power-Expected-Posterior Prior

Dimitris Fouskakis

Ioannis Ntzoufras

UScrime_data function

This data frame contains the following columns:<dl>
<dt><code>M</code></dt>
<dd>percentage of males aged 14--24.</dd><dt><code>Ed</code></dt>
<dd>mean years of schooling.</dd><dt><code>Po1</code></dt>
<dd>police expenditure in 1960.</dd><dt><code>Po2</code></dt>
<dd>police expenditure in 1959.</dd><dt><code>LF</code></dt>
<dd>labour force participation rate.</dd><dt><code>M.F</code></dt>
<dd>number of males per 1000 females.</dd><dt><code>Pop</code></dt>
<dd>state population.</dd><dt><code>NW</code></dt>
<dd>number of non-whites per 1000 people.</dd><dt><code>U1</code></dt>
<dd>unemployment rate of urban males 14--24.</dd><dt><code>U2</code></dt>
<dd>unemployment rate of urban males 35--39.</dd><dt><code>GDP</code></dt>
<dd>gross domestic product per head.</dd><dt><code>Ineq</code></dt>
<dd>income inequality.</dd><dt><code>Prob</code></dt>
<dd>probability of imprisonment.</dd><dt><code>Time</code></dt>
<dd>average time served in state prisons.</dd><dt><code>y</code></dt>
<dd>rate of crimes in a particular category per head of population.</dd>
</dl>

Format

US Crime Data — UScrime_data

This data frame contains the following columns:<dl>
<dt><code>M</code></dt>
<dd>percentage of males aged 14--24.</dd>

<dt><code>Ed</code></dt>
<dd>mean years of schooling.</dd>

<dt><code>Po1</code></dt>
<dd>police expenditure in 1960.</dd>

<dt><code>Po2</code></dt>
<dd>police expenditure in 1959.</dd>

<dt><code>LF</code></dt>
<dd>labour force participation rate.</dd>

<dt><code>M.F</code></dt>
<dd>number of males per 1000 females.</dd>

<dt><code>Pop</code></dt>
<dd>state population.</dd>

<dt><code>NW</code></dt>
<dd>number of non-whites per 1000 people.</dd>

<dt><code>U1</code></dt>
<dd>unemployment rate of urban males 14--24.</dd>

<dt><code>U2</code></dt>
<dd>unemployment rate of urban males 35--39.</dd>

<dt><code>GDP</code></dt>
<dd>gross domestic product per head.</dd>

<dt><code>Ineq</code></dt>
<dd>income inequality.</dd>

<dt><code>Prob</code></dt>
<dd>probability of imprisonment.</dd>

<dt><code>Time</code></dt>
<dd>average time served in state prisons.</dd>

<dt><code>y</code></dt>
<dd>rate of crimes in a particular category per head of population.</dd>


</dl>

UScrime_data: US Crime Data

Description

Arguments

Format