kNN.plot: Visualizing the Optimal Number of k

Description

Visualizing the Optimal Number of k for k-Nearest Neighbour ClassificationkNN based on accuracy or Mean Square Error (MSE).

Usage

kNN.plot(formula, train, test, k.max = 10, transform = FALSE, base = "error", 
         report = FALSE, set.seed = NULL, ...)

Arguments

formula: a formula, with a response but no interaction terms. For the case of data frame, it is taken as the model frame (see model.frame).
train: data frame or matrix of train set cases.
test: data frame or matrix of test set cases.
k.max: the maximum number of number of neighbours to consider, must be at least two.
transform: a character with options FALSE (default), "minmax", and "zscore". Option "minmax" means no transformation. This option allows the users to use normalized version of the train and test sets for the kNN aglorithm.
base: base measurement: error (default), accuracy, or MSE for Mean Square Error.
report: a character with options FALSE (default) and TRUE. Option TRUE reports the values of the base measurement.
set.seed: a single value, interpreted as an integer, or NULL.
...: options to be passed to kNN().

Author

Reza Mohammadi a.mohammadi@uva.nl and Kevin Burke kevin.burke@ul.ie

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

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

Examples

Run this code

data(risk)

train = risk[  1:150, ]
test  = risk[151:246, ]

kNN.plot(risk ~ income + age, train = train, test = test)
kNN.plot(risk ~ income + age, train = train, test = test, base = "accuracy")