funs.scct {nnspat}

Species Correspondence Contingency Table (SCCT)

Description

Two functions: scct.ct and scct.

Both functions return the k \times 2 species correspondence contingency table (SCCT) but have different arguments (see the parameter list below).

SCCT is constructed by categorizing the NN pairs according to pair type as self or mixed. A base-NN pair is called a self pair, if the elements of the pair are from the same class; a mixed pair, if the elements of the pair are from different classes. Row labels in the RCT are the class labels and the column labels are "self" and "mixed". The k \times 2 SCCT (whose first column is self column with entries S_i and second column is mixed with entries M_i) is closely related to the k \times k nearest neighbor contingency table (NNCT) whose entries are N_{ij}, where S_i=N_{ii} and M_i=n_i-N_{ii} with n_i is the size of class i.

The function scct.ct returns the SCCT given the inter-point distance (IPD) matrix or data set x, and the function scct returns the SCCT given the IPD matrix. SCCT is a k \times 2 matrix where k is number of classes in the data set. (See Ceyhan (2018) for more detail, where SCCT is labeled as CCT for correspondence contingency table).

The argument ties is a logical argument (default=FALSE for both functions) 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 nnct is a logical argument for scct.ct only (default=FALSE) to determine the structure of the argument x. If TRUE, x is taken to be the k \times k NNCT, and if FALSE, x is taken to be the IPD matrix.

The argument lab is the vector of class labels (default=NULL when nnct=TRUE in the function scct.ct and no default specified for scct).

Usage

scct.ct(x, lab = NULL, ties = FALSE, nnct = FALSE)

scct(dat, lab, ties = FALSE, ...)

Arguments

x

The IPD matrix (if nnct=FALSE) or the NNCT (if nnct=TRUE), used in scct.ct only

lab

The vector of class labels (numerical or categorical), default=NULL when nnct=FALSE in the function scct.ct and no default specified for scct.

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.

nnct

A logical parameter (default=FALSE). If TRUE, x is taken to be the k \times k NNCT, and if FALSE, x is taken to be the IPD matrix, used in scct.ct only.

dat

The data set in one or higher dimensions, each row corresponds to a data point, used in scct only

...

are for further arguments, such as method and p, passed to the dist function, used in scct only

Value

Returns the k \times 2 SCCT where k is the number of classes in the data set.

Author(s)

Elvan Ceyhan

References

Ceyhan E (2018). “A contingency table approach based on nearest neighbor relations for testing self and mixed correspondence.” SORT-Statistics and Operations Research Transactions, 42(2), 125-158.

See Also

nnct, tct, rct and Qsym.ct

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<-nnct(ipd,cls)
NNCT

scct(Y,cls)
scct(Y,cls,method="max")

scct.ct(ipd,cls)
scct.ct(ipd,cls,ties = TRUE)
scct.ct(NNCT,nnct=TRUE)

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

#############
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<-nnct(ipd,cls)
NNCT

scct(Y,cls)

scct.ct(ipd,cls)
scct.ct(NNCT,nnct=TRUE)