funs.kNNdist2cl: Functions for the k^{th} and 'k' NN distances

In nnspat: Nearest Neighbor Methods for Spatial Patterns

Functions for the k^{th} and k NN distances

Description

Two functions: kthNNdist2cl and kNNdist2cl.

kthNNdist2cl returns the distances between subjects from class i and their k^{th} NNs from class j. The output is a list with first entry (kth.nndist) is an n_i \times 3 matrix where n_i is the size of class i and first column is the subject index for class i, second column is the index of the k^{th} NN of class i subjects among class j subjects and third column contains the corresponding k^{th} NN distances. The other entries in the list are labels of base class and NN class and the value of k, respectively.

kNNdist2cl returns the distances between subjects from class i and their k NNs from class j. The output is a list with first entry (ind.knndist) is an n_i \times (k+1) matrix where n_i is the size of class i, first column is the indices of class i subjects, 2nd to (k+1)-st columns are the indices of k NNs of class i subjects among class j subjects. The second list entry (knndist) is an n_i \times k matrix where n_i is the size of class i and the columns are the kNN distances of class i subjects to class j subjects. The other entries in the list are labels of base class and NN class and the value of k, respectively.

The argument within.class.ind is a logical argument (default=FALSE) to determine the indexing of the class i subjects. If TRUE, index numbering of subjects is within the class, from 1 to class size (i.e., 1:n_i), according to their order in the original data; otherwise, index numbering within class is just the indices in the original data.

The argument is.ipd is a logical argument (default=TRUE) to determine the structure of the argument x. If TRUE, x is taken to be the inter-point distance (IPD) matrix, and if FALSE, x is taken to be the data set with rows representing the data points.

Usage

kthNNdist2cl(x, k, i, j, lab, within.class.ind = FALSE, is.ipd = TRUE, ...)

kNNdist2cl(x, k, i, j, lab, within.class.ind = FALSE, is.ipd = TRUE, ...)

Arguments

x	The IPD matrix (if is.ipd=TRUE) or a data set of points in matrix or data frame form where points correspond to the rows (if is.ipd = FALSEALSE).
k	Integer specifying the number of NNs (of subjects).
i, j	class label of base class and NN classes, respectively.
lab	The vector of class labels (numerical or categorical)
within.class.ind	A logical parameter (default=FALSE). If TRUE, index numbering of subjects is within the class, from 1 to class size (i.e., 1:n_i), according to their order in the original data; otherwise, index numbering within class is just the indices in the original data.
is.ipd	A logical parameter (default=TRUE). If TRUE, x is taken as the inter-point distance matrix, otherwise, x is taken as the data set with rows representing the data points.
...	are for further arguments, such as method and p, passed to the dist function.

Value

kthNNdist2cl returns the list of elements

kth.nndist	n_i \times 3 matrix where n_i is the size of class i and first column is the subject index for class i, second column is the index of the k-th NN of class i subjects among class j subjects and third column contains the corresponding k-th NN distances, , returned by Zseg.ind.ct only
base.class	label of base class
nn.class	label of NN class
k	value of k in kNN

kNNdist2cl returns the list of elements

ind.knndist	n_i \times (k+1) matrix where n_i is the size of class i, first column is the indices of class i subjects, 2nd to (k+1)-st columns are the indices of k NNs of class i subjects among class j subjects.
knndist	n_i \times k matrix where n_i is the size of class i and the columns are the kNN distances of class i subjects to class j subjects.
base.class	label of base class
nn.class	label of NN class
k	value of k in kNN

Author(s)

Elvan Ceyhan

See Also

NNdist2cl, kthNNdist and kNNdist

Examples

#Examples for kthNNdist2cl
#3D data points
n<-20  #or try sample(1:20,1)
Y<-matrix(runif(3*n),ncol=3)
ipd<-ipd.mat(Y)
#two class case
clab<-sample(1:2,n,replace=TRUE) #class labels
table(clab)
kthNNdist2cl(ipd,3,1,2,clab)
kthNNdist2cl(Y,3,1,2,clab,is.ipd = FALSE)
kthNNdist2cl(ipd,3,1,2,clab,within = TRUE)

#three class case
clab<-sample(1:3,n,replace=TRUE) #class labels
table(clab)
kthNNdist2cl(ipd,3,2,3,clab)

#1D data points
n<-15
X<-as.matrix(runif(n))# need to be entered as a matrix with one column
#(i.e., a column vector), hence X<-runif(n) would not work
ipd<-ipd.mat(X)
#two class case
clab<-sample(1:2,n,replace=TRUE) #class labels
table(clab)
kthNNdist2cl(ipd,3,1,2,clab) # here kthNNdist2cl(ipd,3,1,12,clab) #gives an error message

kthNNdist2cl(ipd,3,"1",2,clab)

#Examples for kNNdist2cl
#3D data points
n<-20  #or try sample(1:20,1)
Y<-matrix(runif(3*n),ncol=3)
ipd<-ipd.mat(Y)
#two class case
clab<-sample(1:2,n,replace=TRUE) #class labels
table(clab)
kNNdist2cl(ipd,3,1,2,clab)
kNNdist2cl(Y,3,1,2,clab,is.ipd = FALSE)

kNNdist2cl(ipd,3,1,2,clab,within = TRUE)

#three class case
clab<-sample(1:3,n,replace=TRUE) #class labels
table(clab)
kNNdist2cl(ipd,3,1,2,clab)

#1D data points
n<-15
X<-as.matrix(runif(n))# need to be entered as a matrix with one column
#(i.e., a column vector), hence X<-runif(n) would not work
ipd<-ipd.mat(X)
#two class case
clab<-sample(1:2,n,replace=TRUE) #class labels
table(clab)

kNNdist2cl(ipd,3,1,2,clab)
kNNdist2cl(ipd,3,"1",2,clab) #here kNNdist2cl(ipd,3,"a",2,clab) #gives an error message

nnspat documentation built on Aug. 30, 2022, 9:06 a.m.