ind.nnsym

Returns the index matrix for choosing the entries in the covariance matrix for NNCT 
used for computing the covariance for Dixon's NN symmetry test.
The matrix is \(k(k-1)/2 \times 2\) with each row is the \(i,j\) 
corresponding to \(N_{ij}\) in the NNCT.

Contains the functions for testing the spatial patterns (of segregation, spatial symmetry,
association, disease clustering, species correspondence, and reflexivity) based on nearest neighbor relations,
especially using contingency tables such as
nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and
Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein),
nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>),
species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018)
<doi:10.2436/20.8080.02.72> for two (or higher) dimensional data.
The package also contains functions for generating patterns of segregation, association,
uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>),
and various non-random labeling patterns for disease clustering
in two dimensional cases (Ceyhan (2014)
<doi:10.1002/sim.6053>), and for visualization of all these patterns
for the two dimensional data.
The tests are usually (asymptotic) normal z-tests or chi-square tests.

Elvan Ceyhan

nnspat

Nearest Neighbor Methods for Spatial Patterns

ind.nnsym function

<dl><dt>k</dt>
<dd>An integer specifying the number of classes in the data set</dd></dl>

Arguments

Author

Index Matrix for Computing the Covariance of Dixon's Overall NN Symmetry Test — ind.nnsym

<dl>

<dt>k</dt>
<dd>An integer specifying the number of classes in the data set</dd>

</dl>

ind.nnsym: Index Matrix for Computing the Covariance of Dixon's Overall NN Symmetry Test

Description

Usage

Value

Arguments

Author

See Also