Data from a study measuring health and social characteristics of 
 representative samples of Danish 70 year olds, taken in 1967 and 1984.

datasets

Bayesian networks with continuous and/or discrete
variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.

Claus Dethlefsen

deal

Learning Bayesian Networks with Mixed Variables

ksl function

A data frame with variables of both discrete and continuous
 types.<dl>
 <dt>FEV</dt>
<dd>Forced ejection volume</dd> <dt>Kol</dt>
<dd>Cholesterol</dd> <dt>Hyp</dt>
<dd>Hypertension (no/yes)</dd> <dt>logBMI</dt>
<dd>Logarithm of Body Mass Index</dd> <dt>Smok</dt>
<dd>Smoking (no/yes)</dd> <dt>Alc</dt>
<dd>Alcohol consumption (seldom/frequently)</dd> <dt>Work</dt>
<dd>Working (yes/no)</dd> <dt>Sex</dt>
<dd>male/female</dd> <dt>Year</dt>
<dd>Survey year (1967/1984)</dd> 
</dl>

Format

Health and social characteristics — ksl

A data frame with variables of both discrete and continuous
 types.<dl>
 <dt>FEV</dt>
<dd>Forced ejection volume</dd>

 <dt>Kol</dt>
<dd>Cholesterol</dd>

 <dt>Hyp</dt>
<dd>Hypertension (no/yes)</dd>

 <dt>logBMI</dt>
<dd>Logarithm of Body Mass Index</dd>

 <dt>Smok</dt>
<dd>Smoking (no/yes)</dd>

 <dt>Alc</dt>
<dd>Alcohol consumption (seldom/frequently)</dd>

 <dt>Work</dt>
<dd>Working (yes/no)</dd>

 <dt>Sex</dt>
<dd>male/female</dd>

 <dt>Year</dt>
<dd>Survey year (1967/1984)</dd>

 
</dl>

ksl: Health and social characteristics

Description

Arguments

Format