BayesVarSel (version 1.6.2)

Ozone35: Ozone35 dataset

Description

Polution data

Usage

data(Ozone35)

Format

A data frame with 178 observations on the following 36 variables.

y

Response = Daily maximum 1-hour-average ozone reading (ppm) at Upland, CA

x4

500-millibar pressure height (m) measured at Vandenberg AFB

x5

Wind speed (mph) at Los Angeles International Airport (LAX)

x6

Humidity (percentage) at LAX

x7

Temperature (Fahrenheit degrees) measured at Sandburg, CA

x8

Inversion base height (feet) at LAX

x9

Pressure gradient (mm Hg) from LAX to Daggett, CA

x10

Visibility (miles) measured at LAX

x4.x4

=x4*x4

x4.x5

=x4*x5

x4.x6

=x4*x6

x4.x7

=x4*x7

x4.x8

=x4*x8

x4.x9

=x4*x9

x4.x10

=x4*x10

x5.x5

=x5*x5

x5.x6

=x5*x6

x5.x7

=x5*x7

x5.x8

=x5*x8

x5.x9

=x5*x9

x5.x10

=x5*x10

x6.x6

=x6*x6

x6.x7

=x6*x7

x6.x8

=x6*x8

x6.x9

=x6*x9

x6.x10

=x6*x10

x7.x7

=x7*x7

x7.x8

=x7*x8

x7.x9

=x7*x9

x7.x10

=x7*x10

x8.x8

=x8*x8

x8.x9

=x8*x9

x8.x10

=x8*x10

x9.x9

=x9*x9

x9.x10

=x9*x10

x10.x10

=x10*x10

Details

This dataset has been used by Garcia-Donato and Martinez-Beneito (2013) to illustrate the potential of the Gibbs sampling method (in BayesVarSel implemented in GibbsBvs). This data were previously used by Casella and Moreno (2006) and Berger and Molina (2005) and concern N = 178 measures of ozone concentration in the atmosphere. Of the 10 main effects originally considered, we only make use of those with an atmospheric meaning x4 to x10, as was done by Liang et al. (2008). We then have 7 main effects which, jointly with the quadratic terms and second order interactions, produce the above-mentioned p = 35 possible regressors.

References

Berger, J. and Molina, G. (2005) Posterior model probabilities via path-based pairwise priors. Statistica Neerlandica, 59:3-15. Casella, G. and Moreno, E. (2006) Objective Bayesian variable selection. Journal of the American Statistical Association, 101(473). Garcia-Donato, G. and Martinez-Beneito, M.A. (2013) On sampling strategies in Bayesian variable selection problems with large model spaces. Journal of the American Statistical Association, 108: 340-352. Liang, F., Paulo, R., Molina, G., Clyde, M. and Berger, J.O. (2008) Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association. 103:410-423.

Examples

data(Ozone35)