traffic

This data set contains 108 observations on 6 variables.

datasets

Contains functions to perform Bayesian inference
using a spectral analysis of Gaussian process priors.
Gaussian processes are represented with a Fourier series
based on cosine basis functions. Currently the package
includes parametric linear models, partial linear additive
models with/without shape restrictions, generalized linear
additive models with/without shape restrictions, and
density estimation model. To maximize computational
efficiency, the actual Markov chain Monte Carlo sampling
for each model is done using codes written in FORTRAN 90.

Beomjo Park

bsamGP

Bayesian Spectral Analysis Models using Gaussian Process Priors

traffic function

<dl class="dl-horizontal">
<dt><code>ln_number</code></dt><dd>logarithm of the number of monthly automobile accidents in the state of Michigan.</dd>
<dt><code>month</code></dt><dd>months from January 1st, 1979 to Decembe 31st, 1987.</dd>
<dt><code>ln_unemp</code></dt><dd>logarithm of unemployment rate</dd>
<dt><code>spring</code></dt><dd>indicator for spring season.</dd>
<dt><code>summer</code></dt><dd>indicator for summer season.</dd>
<dt><code>fall</code></dt><dd>indicator for fall season.</dd>
</dl>

Format

Monthly traffic accidents data — traffic

<dl class='dl-horizontal'>
<dt><code>ln_number</code></dt><dd>logarithm of the number of monthly automobile accidents in the state of Michigan.</dd>
<dt><code>month</code></dt><dd>months from January 1st, 1979 to Decembe 31st, 1987.</dd>
<dt><code>ln_unemp</code></dt><dd>logarithm of unemployment rate</dd>
<dt><code>spring</code></dt><dd>indicator for spring season.</dd>
<dt><code>summer</code></dt><dd>indicator for summer season.</dd>
<dt><code>fall</code></dt><dd>indicator for fall season.</dd>
</dl>

traffic: Monthly traffic accidents data

Description

Usage

Arguments

Format

References

Examples