gencve (version 0.3)

kNN_MLE: MLE k in kNN

Description

Uses the profile pseudolikelihood to obtain the estimate for k, the number of nearest neighbors parameter in kNN.

Usage

kNN_MLE(X, Y, kmax = ceiling(length(Y) * 0.5), plot = TRUE)

Arguments

X

An n-by-p matrix of covariates

Y

Outputs with Q classes

kmax

The maximum size of k

plot

if TRUE, plot the profile deviance otherwise no plot

Value

The estimate of k obtained by maximizing the pseudolikelihood is returned. It can take any value from k=0 to k=kmax.The result is returned invisibly if plot is TRUE.

Details

When Q=2, the glm algorithm is used to compute the profile pseudologlikelihood and for Q>2, the function multinom in nnet is used.

References

Holmes, C. C. and Adams, N. M. (2003). Likelihood inference in nearest-neighbour classification models, Biometrika, 90(1), 99-112. http://biomet.oxfordjournals.org/cgi/content/abstract/90/1/99

See Also

multinom

Examples

#Two classes example
X <- MASS::synth.tr[,1:2]
Y <- MASS::synth.tr[,3]
kNN_MLE(X=X, Y=Y, plot=FALSE)

## Not run: 
# #Three classes example
# library("MASS") #need lda
# Y<- iris[,5]
# X<- iris[,1:4]
# kopt <- kNN_MLE(X, Y)
# kopt
# #Mis-classification rates on training data.
# #Of course FLDA does better in this case.
# y <- factor(Y)
# ans <- class::knn(train=X, test=X, k=kopt, cl=y)
# etaKNN <- sum(ans!=y)/length(y)
# iris.ldf <- MASS::lda(X, y)
# yfitFLDA <- MASS::predict.lda(iris.ldf, newdata=X, dimen=1)$class
# etaFLDA <- sum(yfitFLDA!=y)/length(y)
# eta<-c(etaFLDA, etaKNN)
# names(eta)<-c("FLDA", "kNN")
# eta
# ## End(Not run)