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<table width="100%" summary="page for knn.cv {class}"><tr><td>knn.cv {class}</td><td align="right">R Documentation</td></tr></table>
<h2>k-Nearest Neighbour Cross-Validatory Classification</h2>
<h3>Description</h3>
<p>
k-nearest neighbour cross-validatory classification from training set.
</p>
<h3>Usage</h3>
<pre>
knn.cv(train, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>train</code></td>
<td>
matrix or data frame of training set cases.
</td></tr>
<tr valign="top"><td><code>cl</code></td>
<td>
factor of true classifications of training set
</td></tr>
<tr valign="top"><td><code>k</code></td>
<td>
number of neighbours considered.
</td></tr>
<tr valign="top"><td><code>l</code></td>
<td>
minimum vote for definite decision, otherwise <code>doubt</code>. (More
precisely, less than <code>k-l</code> dissenting votes are allowed, even
if <code>k</code> is increased by ties.)
</td></tr>
<tr valign="top"><td><code>prob</code></td>
<td>
If this is true, the proportion of the votes for the winning class
are returned as attribute <code>prob</code>.
</td></tr>
<tr valign="top"><td><code>use.all</code></td>
<td>
controls handling of ties. If true, all distances equal to the <code>k</code>th
largest are included. If false, a random selection of distances
equal to the <code>k</code>th is chosen to use exactly <code>k</code> neighbours.
</td></tr>
</table>
<h3>Details</h3>
<p>
This uses leave-one-out cross validation.
For each row of the training set <code>train</code>, the <code>k</code> nearest
(in Euclidean distance) other
training set vectors are found, and the classification is decided by
majority vote, with ties broken at random. If there are ties for the
<code>k</code>th nearest vector, all candidates are included in the vote.
</p>
<h3>Value</h3>
<p>
factor of classifications of training set. <code>doubt</code> will be returned as <code>NA</code>.</p>
<h3>References</h3>
<p>
Ripley, B. D. (1996)
<EM>Pattern Recognition and Neural Networks.</EM> Cambridge.
</p>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>See Also</h3>
<p>
<code><a href="knn.html">knn</a></code>
</p>
<h3>Examples</h3>
<pre>
data(iris3)
train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
cl <- factor(c(rep("s",50), rep("c",50), rep("v",50)))
knn.cv(train, cl, k = 3, prob = TRUE)
attributes(.Last.value)
</pre>
<hr><div align="center">[Package <em>class</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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