knnflex-package: A more flexible KNN

In Dasonk/knnflex: A more flexible KNN

Description

A K-Nearest Neighbor (KNN) implementation which allows the specification of the distance used to calculate nearest neighbors (euclidean, binary, etc.), the aggregation method used to summarize response (majority class, mean, etc.) and the method of handling ties (all, random selection, etc.). Additionally, responses are not limited to classes, but can be continuous.

Note

This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1 P20 HG003900-01. Information on the Molecular Libraries Roadmap Initiative can be obtained from http://nihroadmap.nih.gov/molecularlibraries/.

Author(s)

Atina Dunlap Brooks

See Also

knn

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))
x  <- cbind(x1,x2)
y <- c(rep(1,20),rep(0,20))
train <- sample(1:40,30)
# plot the training cases
plot(x1[train],x2[train],col=y[train]+1,xlab="x1",ylab="x2")
# 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)


# the iris example used by knn(class)
library(class)
data(iris3)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
# how to get predictions from knn(class)
pred<-knn(train, test, cl, k = 3)
# display the confusion matrix
table(pred,cl)

# how to get predictions with knn.dist and knn.predict
x <- rbind(train,test)
kdist <- knn.dist(x)
pred <- knn.predict(1:75, 76:150, cl, kdist, k=3)
# display the confusion matrix
table(pred,cl)

# note any small differences are a result of both methods
# breaking ties in majority class randomly


# 5-fold cross-validation to select k for above example
fold <- sample(1:5,75,replace=TRUE)
cvpred <- matrix(NA,nrow=75,ncol=10)
for (k in 1:10)
  for (i in 1:5)
    cvpred[which(fold==i),k] <- knn.predict(train=which(fold!=i),test=which(fold==i),cl,kdist,k=k)
# display misclassification rates for k=1:10
apply(cvpred,2,function(x) sum(cl!=x))

Dasonk/knnflex documentation built on May 6, 2019, 1:37 p.m.