Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.

ROC for Cross Validation Results

package-ubuntu-20.04-r-4.0.5#cran#roccv-1.2

<table summary="R argblock">
<tr valign="top">
<td><code>n</code></td>
<td>
<p>sample size</p>
</td>
</tr>
<tr valign="top">
<td><code>k</code></td>
<td>
<p>number of groups</p>
</td>
</tr>
</table>

<p>returns a vector of length n with a random assignment of entries from 1 to k
</p>

<p>Ben Sherwood &lt;ben.sherwood@ku.edu&gt;
</p>

n <- 100
folds_10 <- randomly_assign(n,10)
folds_5 <- randomly_assign(n,5)

package-ubuntu-20.04-r-4.0.5#cran#roccv-1.2#docs#randomly_assign

randomly_assign

Description

Usage

Arguments

Value

Author(s)

Examples

roccv

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`n`	sample size
`k`	number of groups