sniffer

Data examining the factors that impact the amount of hydrocarbon vapour released
when gasoline is pumped into a tank.

datasets

Implementation of the SIC epsilon-telescope method, either
using single or distributional (multiparameter) regression. Includes classical regression
with normally distributed errors and robust regression, where the errors are from
the Laplace distribution. The "smooth generalized normal distribution" is used,
where the estimation of an additional shape parameter allows the user to move
smoothly between both types of regression. See O'Neill and Burke (2022)
"Robust Distributional Regression with Automatic Variable Selection" for more details.
<arXiv:2212.07317>. This package also contains the data analyses from O'Neill and
Burke (2023). "Variable selection using a smooth information criterion for distributional
regression models". <doi:10.1007/s11222-023-10204-8>.

Meadhbh O'Neill

smoothic

Variable Selection Using a Smooth Information Criterion

Kevin Burke

sniffer function

A data frame with 125 rows and 5 variables:<dl>
 <dt>tanktemp</dt>
<dd>initial tank temperature (degrees F)</dd> <dt>gastemp</dt>
<dd>temperature of the dispensed gasoline (degrees F)</dd> <dt>tankpres</dt>
<dd>initial vapour pressure in the tank (psi)</dd> <dt>gaspres</dt>
<dd>vapour pressure of the dispensed gasoline (psi)</dd> <dt>y</dt>
<dd>hydrocarbons emitted (g)</dd> 
</dl>

Format

Sniffer Data — sniffer

A data frame with 125 rows and 5 variables:<dl>
 <dt>tanktemp</dt>
<dd>initial tank temperature (degrees F)</dd>

 <dt>gastemp</dt>
<dd>temperature of the dispensed gasoline (degrees F)</dd>

 <dt>tankpres</dt>
<dd>initial vapour pressure in the tank (psi)</dd>

 <dt>gaspres</dt>
<dd>vapour pressure of the dispensed gasoline (psi)</dd>

 <dt>y</dt>
<dd>hydrocarbons emitted (g)</dd>

 
</dl>

sniffer: Sniffer Data

Description

Usage

Arguments

Format

References