| funsW345values | R Documentation |
W_k values for Tango's T test statisticThree functions: W3val, W4val and W5val, each of which is needed to compute E[T^3]
(i.e., for the skewness of T)
where T=T(\theta) which is defined in Equation (2) of \insertCitetango:2007;textualnnspat as follows:
Let (z_1,\ldots,z_n ), n = n_0 + n_1, denote the locations of the points in the combined sample
when the indices have been randomly permuted so that the z_i contain no information about group membership.
T(\theta)=\sum_{i=1}^{n}\sum_{j=1}^{n}\delta_i \delta_j a_{ij}(\theta)=
\boldsymbol \delta^t \boldsymbol A(\theta)) \boldsymbol \delta
where \delta_i=1 if z_i is a case,
and 0 if z_i is a control, \boldsymbol A(\theta) = (a_{ij} (\theta)) could be any matrix of a measure of
the closeness between two points i and j with a_{ii} = 0 for all i = 1,\ldots,n, and \boldsymbol \theta =
(\theta_1,\ldots,\theta_p)^t denotes the unknown parameter vector related to cluster size and
\boldsymbol \delta = (\delta_1,\ldots,\delta_n)^t.
Here the number of cases are denoted as n_1 and number of controls as n_0 to match the case-control class
labeling, which is just the reverse of the labeling in \insertCitetango:2007;textualnnspat.
If \theta=k in the nearest neighbors model with a_{ij}(k) = 1 if z_j is among the kNNs of z_i and 0
otherwise, then the test statistic T(\theta) = T_k is the Cuzick and Edwards kNN test statistic, T_k
\insertCitecuzick:1990;textualnnspat, see also ceTk.
W_k values are used for Tango's correction to Cuzick and Edwards kNN test statistic, T_k and
W_k here corresponds to W_{k-1} in \insertCitetango:2007;textualnnspat
(defined for consistency with p_k's and alpha_r having r distinct elements).
The argument of the function is the A_{ij} matrix, a, which is the output of the function aij.mat.
However, inside the function we symmetrize the matrix a as b <- (a+a^t)/2, to facilitate the formulation.
W3val(a)
W4val(a)
W5val(a)
a |
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Each function Wkval returns the W_k value for k=3,4,5.
Elvan Ceyhan
ceTk, EV.Tk, varTk, Xsq.ceTk
n<-20 #or try sample(1:20,1)
Y<-matrix(runif(3*n),ncol=3)
k<-sample(1:5,1) # try also 3, 5, sample(1:5,1)
k
a<-aij.mat(Y,k)
W3val(a)
W4val(a)
W5val(a)
a<-aij.mat(Y,k,method="max")
W3val(a)
W4val(a)
W5val(a)
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