nnspat (version 0.1.2)

NNdist2cl: Distances between subjects from class \(i\) and their NNs from class \(j\)

Description

Returns the distances between subjects from class \(i\) and their nearest neighbors (NNs) from class \(j\). The output is a list with first entry (nndist) being an \(n_i \times 3\) matrix where \(n_i\) is the size of class \(i\) and first column is the subject index in class \(i\), second column is the subject index in NN class \(j\), and third column contains the corresponding distances of each class \(i\) subject to its NN among class \(j\) subjects. Class \(i\) is labeled as base class and class \(j\) is labeled as NN class.

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

NNdist2cl(x, i, j, lab, within.class.ind = FALSE, is.ipd = TRUE, ...)

Value

Returns a list with three elements

nndist

\(n_i \times 3\) matrix where \(n_i\) is the size of class \(i\) and first column is the subject index in class \(i\), second column is the subject index in NN class \(j\), and third column contains the corresponding distances of each class \(i\) subject to its NN among class \(j\) subjects.

base.class

label of base class

nn.class

label of NN class

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 = FALSE).

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.

Author

Elvan Ceyhan

See Also

kthNNdist, kNNdist, and NNdist2cl

Examples

#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)
NNdist2cl(ipd,1,2,clab)
NNdist2cl(Y,1,2,clab,is.ipd = FALSE)

NNdist2cl(ipd,1,2,clab,within = TRUE)

#three class case
clab<-sample(1:3,n,replace=TRUE) #class labels
table(clab)
NNdist2cl(ipd,2,1,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)
NNdist2cl(ipd,1,2,clab)
NNdist2cl(X,1,2,clab,is.ipd = FALSE)