nnspat (version 0.1.2)

funs.scct: 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:NNCorrespond2018;textualnnspat 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, ...)

Value

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

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

Author

Elvan Ceyhan

References

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)