This data set consists of 654 observations on youths aged 3 to 19 from
East Boston recorded duing the middle to late 1970's.
Forced expiratory volume (FEV), a measure of lung capacity, is the
variable of interest. Age and height are two continuous predictors.
Sex and smoke are two categorical predictors.

datasets

Model stability and variable inclusion plots [Mueller and Welsh
(2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
(2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
(2008, <doi:10.1214/07-AOS517>); Jiang et al.
(2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.

Garth Tarr

mplot

Graphical Model Stability and Variable Selection Procedures

Samuel Mueller

Alan H Welsh

fev function

A data frame with 654 observations on 5 variables.<dl class="dl-horizontal">
<dt>age</dt><dd>Age (years)</dd>
<dt>fev</dt><dd>Forced expiratory volume (liters). Roughly the amount
of air an individual can exhale in the first second of
a forceful breath.</dd>
<dt>height</dt><dd>Height (inches).</dd>
<dt>sex</dt><dd>Female is 0. Male is 1.</dd>
<dt>smoke</dt><dd>A binary variable indicating whether or not the
youth smokes. Nonsmoker is 0. Smoker is 1.</dd>
</dl>

Format

Forced Expiratory Volume — fev

A data frame with 654 observations on 5 variables.<dl class='dl-horizontal'>
<dt>age</dt><dd>Age (years)</dd>
<dt>fev</dt><dd>Forced expiratory volume (liters). Roughly the amount
of air an individual can exhale in the first second of
a forceful breath.</dd>
<dt>height</dt><dd>Height (inches).</dd>
<dt>sex</dt><dd>Female is 0. Male is 1.</dd>
<dt>smoke</dt><dd>A binary variable indicating whether or not the
youth smokes. Nonsmoker is 0. Smoker is 1.</dd>
</dl>

fev: Forced Expiratory Volume

Description

Usage

Arguments

Format

Details

References

Examples