KODAMA (version 0.0.1)

knn.predict: KNN Prediction Routine using Pre-Calculated Distances

Description

K-Nearest Neighbor prediction method which uses the distances calculated by knn.dist.

Usage

knn.predict(train, test, y, dist.matrix, k=1, agg.meth=if (is.factor(y)) "majority" else "mean", ties.meth="min")

Arguments

train

indexes which specify the rows of the dist.matrix to use as training set.

test

indexes which specify the rows of the dist.matrix to use as test set.

y

a vector of labels.

dist.matrix

the output from a call to knn.dist.

k

the number of nearest neighbors to consider.

agg.meth

method to combine responses of the nearest neighbors, defaults to "majority" for classification and "mean" for continuous responses.

ties.meth

method to handle ties for the k-th neighbor, the default is "min" which uses all ties, alternatives include "max" which uses none if there are ties for the k-th nearest neighbor, "random" which selects among the ties randomly and "first" which uses the ties in their order in the data.

Value

A vector of predictions whose length is the number of test cases.

Details

Predictions are calculated for each test case by aggregating the responses of the k-nearest neighbors among the training cases. k may be specified to be any positive integer less than the number of training cases, but is generally between 1 and 10. The indexes for the training and test cases are in reference to the order of the entire data set as it was passed to knn.dist. The aggregation may be any named function. By default, classification (factored responses) will use the majority class function and non-factored responses will use mean. Other options to consider include min, max, and median. The ties are handled using the rank function. Further information may be found by examining the ties.method there.

See Also

knn.dist,dist

Examples

	#a quick classification example
	x1 <- c(rnorm(20, mean=1), rnorm(20, mean=5))
	x2 <- c(rnorm(20, mean=5), rnorm(20, mean=1))
	y=rep(1:2,each=20)
	x <- cbind(x1,x2)
	train <- sample(1:40, 30)
	#plot the training cases
	plot(x1[train], x2[train], col=y[train]+1)
	#predict the other cases
	test <- (1:40)[-train]
	kdist <- knn.dist(x)
	preds <- knn.predict(train, test, y ,kdist, k=3, agg.meth="majority")
	#add the predictions to the plot
	points(x1[test], x2[test], col=as.integer(preds)+1, pch="+")
	#display the confusion matrix
	table(y[test], preds)