nnspat (version 0.1.2)

nnct: Nearest Neighbor Contingency Table (NNCT)

Description

Returns the \(k \times k\) NNCT given the IPD matrix or data set x where \(k\) is the number of classes in the data set. Rows and columns of the NNCT are labeled with the corresponding class labels.

The argument ties is a logical argument (default=FALSE) to take ties into account or not. If TRUE a NN contributes \(1/m\) to the NN count if it is one of the \(m\) tied NNs of a subject.

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.

See also (dixon:1994,dixon:NNCTEco2002,ceyhan:eest-2010,ceyhan:jkss-posthoc-2017;textualnnspat) and the references therein.

Usage

nnct(x, lab, ties = FALSE, is.ipd = TRUE, ...)

Value

Returns the \(k \times k\) NNCT where \(k\) is the number of classes in the data set.

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).

lab

The vector of class labels (numerical or categorical)

ties

A logical argument (default=FALSE) to take ties into account or not. If TRUE a NN contributes \(1/m\) to the NN count if it is one of the \(m\) tied NNs of a subject.

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

References

See Also

nnct.sub, scct, rct, and tct

Examples

n<-20  #or try sample(1:20,1)
Y<-matrix(runif(3*n),ncol=3)
ipd<-ipd.mat(Y)
cls<-sample(1:2,n,replace = TRUE)  #or try cls<-rep(1:2,c(10,10))
nnct(ipd,cls)
nnct(ipd,cls,ties = TRUE)

nnct(Y,cls,is.ipd = FALSE)
nnct(Y,cls,is.ipd = FALSE,method="max")
nnct(Y,cls,is.ipd = FALSE,method="mink",p=6)

#with one class, it works but really uninformative
cls<-rep(1,n)
nnct(ipd,cls)

#cls as a factor
na<-floor(n/2); nb<-n-na
fcls<-rep(c("a","b"),c(na,nb))
nnct(ipd,fcls)

#cls as an unsorted factor
fcls1<-sample(c("a","b"),n,replace = TRUE)
nnct(ipd,fcls1)

fcls2<-sort(fcls1)
nnct(ipd,fcls2) 
#ipd needs to be sorted as well, otherwise this result will not agree with fcls1

nnct(Y,fcls1,ties = TRUE,is.ipd = FALSE)

#############
n<-40
Y<-matrix(runif(3*n),ncol=3)
ipd<-ipd.mat(Y)
cls<-sample(1:4,n,replace = TRUE)  #or try cls<-rep(1:2,c(10,10))
nnct(ipd,cls)
nnct(Y,cls,is.ipd = FALSE)

#cls as a factor
fcls<-rep(letters[1:4],rep(10,4))
nnct(ipd,fcls)

#1D data points
n<-20  #or try sample(1:20,1)
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)
cls<-sample(1:2,n,replace = TRUE)  #or try cls<-rep(1:2,c(10,10))
nnct(ipd,cls)

#cls as a factor
na<-floor(n/2); nb<-n-na
fcls<-rep(c("a","b"),c(na,nb))
nnct(ipd,fcls)

#with possible ties in the data
Y<-matrix(round(runif(3*n)*10),ncol=3)
ipd<-ipd.mat(Y)
cls<-sample(1:2,n,replace = TRUE)  #or try cls<-rep(1:2,c(10,10))
nnct(ipd,cls)
nnct(ipd,cls,ties = TRUE)