View source: R/NNCTFunctions.r
cov.nnct | R Documentation |
Returns the covariance matrix of cell counts N_{ij} for i,j=1,…,k in the NNCT, ct
.
The covariance matrix is of dimension k^2 \times k^2 and its entries are cov(N_{ij},N_{kl}) when N_{ij} values are
by default corresponding to the row-wise vectorization of ct
. If byrow=FALSE
, the column-wise
vectorization of ct
is used.
These covariances are valid under RL or conditional on Q and R under CSR.
See also (\insertCitedixon:1994,dixon:NNCTEco2002,ceyhan:eest-2010,ceyhan:jkss-posthoc-2017;textualnnspat).
cov.nnct(ct, varN, Q, R, byrow = TRUE)
ct |
A nearest neighbor contingency table |
varN |
The k \times k variance matrix of cell counts of NNCT, |
Q |
The number of shared NNs |
R |
The number of reflexive NNs (i.e., twice the number of reflexive NN pairs) |
byrow |
A logical argument (default= |
The k^2 \times k^2 covariance matrix of cell counts N_{ij} for i,j=1,…,k in the NNCT, ct
Elvan Ceyhan
covNrow2col
, cov.tct
and cov.nnsym
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)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) cov.nnct(ct,varN,Qv,Rv) cov.nnct(ct,varN,Qv,Rv,byrow=FALSE) ############# n<-40 Y<-matrix(runif(3*n),ncol=3) ipd<-ipd.mat(Y) cls<-sample(1:4,n,replace = TRUE) #or try cls<-rep(1:2,c(10,10)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) cov.nnct(ct,varN,Qv,Rv) cov.nnct(ct,varN,Qv,Rv,byrow=FALSE) #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)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) cov.nnct(ct,varN,Qv,Rv)
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