wage.union

This data set contains 534 observations on 11 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.
This software has been developed using funding supported by
Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education
(no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).

Beomjo Park

bsamGP

Bayesian Spectral Analysis Models using Gaussian Process Priors

Seongil Jo

Taeryon Choi

wage.union function

<dl>
<dt><code>education</code></dt>
<dd>number of years of education.</dd><dt><code>south</code></dt>
<dd>indicator of living in southern region of U.S.A.</dd><dt><code>sex</code></dt>
<dd>gender indicator: 0=male,1=female.</dd><dt><code>experience</code></dt>
<dd>number of years of work experience.</dd><dt><code>union</code></dt>
<dd>indicator of trade union membership: 0=non-member, 1=member.</dd><dt><code>wage</code></dt>
<dd>wages in dollars per hour.</dd><dt><code>age</code></dt>
<dd>age in years.</dd><dt><code>race</code></dt>
<dd>1=black, 2=Hispanic, 3=white.</dd><dt><code>occupation</code></dt>
<dd>1=management, 2=sales, 3=clerical, 4=service, 5=professional, 6=other.</dd><dt><code>sector</code></dt>
<dd>0=other, 1=manufacturing, 2=construction.</dd><dt><code>married</code></dt>
<dd>indicator of being married: 0=unmarried, 1=married.</dd>
</dl>

Format

Wage-Union data — wage.union

<dl>
<dt><code>education</code></dt>
<dd>number of years of education.</dd>

<dt><code>south</code></dt>
<dd>indicator of living in southern region of U.S.A.</dd>

<dt><code>sex</code></dt>
<dd>gender indicator: 0=male,1=female.</dd>

<dt><code>experience</code></dt>
<dd>number of years of work experience.</dd>

<dt><code>union</code></dt>
<dd>indicator of trade union membership: 0=non-member, 1=member.</dd>

<dt><code>wage</code></dt>
<dd>wages in dollars per hour.</dd>

<dt><code>age</code></dt>
<dd>age in years.</dd>

<dt><code>race</code></dt>
<dd>1=black, 2=Hispanic, 3=white.</dd>

<dt><code>occupation</code></dt>
<dd>1=management, 2=sales, 3=clerical, 4=service, 5=professional, 6=other.</dd>

<dt><code>sector</code></dt>
<dd>0=other, 1=manufacturing, 2=construction.</dd>

<dt><code>married</code></dt>
<dd>indicator of being married: 0=unmarried, 1=married.</dd>


</dl>

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wage.union: Wage-Union data

Description

Usage

Arguments

Format

References

Examples