knn.ani {animation}R Documentation

Demonstration of the k-Nearest Neighbour classification


Demonstrate the process of k-Nearest Neighbour classification on the 2D plane.


knn.ani(train, test, cl, k = 10, interact = FALSE, tt.col = c("blue", "red"), 
    cl.pch = seq_along(unique(cl)), dist.lty = 2, dist.col = "gray", knn.col = "green", 



matrix or data frame of training set cases containing only 2 columns


matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case. It should also contain only 2 columns. This data set will be ignored if interact = TRUE; see interact below.


factor of true classifications of training set


number of neighbours considered.


logical. If TRUE, the user will have to choose a test set for himself using mouse click on the screen; otherwise compute kNN classification based on argument test.


a vector of length 2 specifying the colors for the training data and test data.


a vector specifying symbols for each class

dist.lty, dist.col

the line type and color to annotate the distances


the color to annotate the k-nearest neighbour points using a polygon


additional arguments to create the empty frame for the animation (passed to plot.default)


For each row of the test set, the k nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. For a single test sample point, the basic steps are:

  1. locate the test point

  2. compute the distances between the test point and all points in the training set

  3. find k shortest distances and the corresponding training set points

  4. vote for the result (find the maximum in the table for the true classifications)

As there are four steps in an iteration, the total number of animation frames should be 4 * min(nrow(test), ani.options('nmax')) at last.


A vector of class labels for the test set.


There is a special restriction (only two columns) on the training and test data set just for sake of the convenience for making a scatterplot. This is only a rough demonstration; for practical applications, please refer to existing kNN functions such as knn in class, etc.

If either one of train and test is missing, there'll be random matrices prepared for them. (It's the same for cl.)


Yihui Xie


Examples at

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also


[Package animation version 2.5 Index]