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<table width="100%" summary="page for wesdr"><tr><td>wesdr</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Diabetic retinopathy in Wisconsin</h2>

<h3>Description</h3>

<p>The data, originally from the <code>gss</code> package, record whether or not diabetic patients developed retinopathy along with three possible predictors.
</p>


<h3>Usage</h3>

<pre>
data(wesdr)
</pre>


<h3>Format</h3>

<p>The <code>wesdr</code> data frame has the following columns
</p>

<dl>
<dt>ret</dt><dd><p>binary variable: 1 = retinopathy, 0 = not.</p>
</dd>
<dt>bmi</dt><dd><p>Body mass index (weight in kg divided by square of height in metres)</p>
</dd>
<dt>gly</dt><dd><p>Glycosylated hemoglobin - the percentage of hemoglobin bound to glucuse in the blood. This reflects long term average blood glucose levels: less than 6% is typical of non-diabetics, but is only rarely acheived by diabetic patients.</p>
</dd>
<dt>dur</dt><dd><p>Duration of disease in years.</p>
</dd>
</dl>



<h3>Details</h3>

<p>Retinopathy is a common problem in diabetic patients and the interst is in predicting the risk using the measured predictors.
</p>


<h3>Source</h3>

<p>Data are from Chong Gu's <code>gss</code> package. 
</p>


<h3>Examples</h3>

<pre>
require(gamair);require(mgcv)
data(wesdr)
## Smooth ANOVA model...
k &lt;- 5
b &lt;- gam(ret~s(dur,k=k)+s(gly,k=k)+s(bmi,k=k)+ti(dur,gly,k=k)+
         ti(dur,bmi,k=k)+ti(gly,bmi,k=k),select=TRUE,
         data=wesdr,family=binomial(),method="ML")
ow &lt;- options(warn=-1) ## avoid complaint about zlim 
plot(b,pages=1,scheme=1,zlim=c(-3,3))
options(ow)
</pre>


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