svmtrain

Support Vector Machine density estimation (1-SVM) was applied to a set
of negative control samples, and then used to test on a positive control.

datasets

An extensible framework
for automatically placing direct labels onto multicolor 'lattice' or
'ggplot2' plots.
Label positions are described using Positioning Methods
which can be re-used across several different plots.
There are heuristics for examining "trellis" and "ggplot" objects
and inferring an appropriate Positioning Method.

Toby Hocking

directlabels

Direct Labels for Multicolor Plots

svmtrain function

A data frame with 378 observations on the following 5 variables.<dl class="dl-horizontal">
 <dt><code>replicate</code></dt><dd>a factor with levels <code>1</code> <code>2</code>
 <code>3</code>, the experimental replicate. We fit 1-SVM models to each
 replicate separately.</dd>
 <dt><code>rate</code></dt><dd>a numeric vector, the percent of observations
 that were outside the trained model.</dd>
 <dt><code>data</code></dt><dd>a factor with levels <code>KIF11</code> <code>test</code>
 <code>train</code>, which set of observations did we measure. test and
 train are each 50% random splits of the negative controls in the
 experiment, and KIF11 is the positive control in the experiment.</dd>
 <dt><code>gamma</code></dt><dd>a numeric vector, the tuning parameter of the
 radial basis function kernel.</dd>
 <dt><code>nu</code></dt><dd>a numeric vector, the regularization parameter
 of the 1-SVM.</dd>
 </dl>

Format

False positive rates from several 1-SVM models — svmtrain

A data frame with 378 observations on the following 5 variables.<dl class='dl-horizontal'>
 <dt><code>replicate</code></dt><dd>a factor with levels <code>1</code> <code>2</code>
 <code>3</code>, the experimental replicate. We fit 1-SVM models to each
 replicate separately.</dd>
 <dt><code>rate</code></dt><dd>a numeric vector, the percent of observations
 that were outside the trained model.</dd>
 <dt><code>data</code></dt><dd>a factor with levels <code>KIF11</code> <code>test</code>
 <code>train</code>, which set of observations did we measure. test and
 train are each 50% random splits of the negative controls in the
 experiment, and KIF11 is the positive control in the experiment.</dd>
 <dt><code>gamma</code></dt><dd>a numeric vector, the tuning parameter of the
 radial basis function kernel.</dd>
 <dt><code>nu</code></dt><dd>a numeric vector, the regularization parameter
 of the 1-SVM.</dd>
 </dl>

svmtrain: False positive rates from several 1-SVM models

Description

Usage

Arguments

Format