View source: R/NNCTFunctions.R

nnct | R Documentation |

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

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

`x` |
The IPD matrix (if |

`lab` |
The |

`ties` |
A logical argument (default= |

`is.ipd` |
A logical parameter (default= |

`...` |
are for further arguments, such as |

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

Elvan Ceyhan

`nnct.sub`

, `scct`

, `rct`

, and `tct`

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)

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