| asyvarTk | R Documentation | 
This function computes the asymptotic variance of Cuzick and Edwards T_k test statistic based on the number
of cases within kNNs of the cases in the data. 
The argument, n_1, is the number of cases (denoted as n1 as an argument).
The number of cases are denoted as n_1 and number of controls as n_0 in this function
to match the case-control class labeling,
which is just the reverse of the labeling in \insertCitecuzick:1990;textualnnspat.
The logical argument nonzero.mat (default=TRUE) is for using the A matrix if FALSE or just the matrix of nonzero
locations in the A matrix (if TRUE) for computing N_s and N_t, which are required in the computation of the
asymptotic variance. N_s and N_t are defined on page 78 of (\insertCitecuzick:1990;textualnnspat) as follows.
N_s=∑_i∑_j a_{ij} a_{ji} (i.e., number of ordered pairs for which kNN relation is symmetric)
and N_t= ∑ ∑_{i \ne l}∑ a_{ij} a_{lj} (i.e, number of triplets (i,j,l) i,j, and l distinct so that
j is among kNNs of i and j is among kNNs of l).
For the A matrix, see the description of the functions aij.mat and aij.nonzero.
See (\insertCitecuzick:1990;textualnnspat) for more details.
asyvarTk(dat, n1, k, nonzero.mat = TRUE, ...)
| dat | The data set in one or higher dimensions, each row corresponds to a data point. | 
| n1 | Number of cases | 
| k | Integer specifying the number of NNs (of subject i) | 
| nonzero.mat | A logical argument (default is  | 
| ... | are for further arguments, such as  | 
A list with the elements
| asy.var | The asymptotic variance of Cuzick and Edwards T_k test statistic for disease clustering | 
| Ns | The N_s value standing for the number of ordered pairs for which  | 
| Nt | The N_t value standing for the number of triplets (i,j,l) i,j, and l distinct so that
j is among  | 
Elvan Ceyhan
ceTk, varTk, and varTkaij
n<-20 #or try sample(1:20,1) Y<-matrix(runif(3*n),ncol=3) cls<-sample(0:1,n,replace = TRUE) #or try cls<-rep(0:1,c(10,10)) n1<-sum(cls==1) k<-3 #try also 2,3 asyvarTk(Y,n1,k) asyvarTk(Y,n1,k,nonzero.mat=FALSE) asyvarTk(Y,n1,k,method="max")
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