euc.dist

Returns the Euclidean distance between <code>x</code> and <code>y</code> 
which can be vectors #' or matrices or data frames of
 any dimension (<code>x</code> and <code>y</code> should be of same dimension).
This function is equivalent to <code>Dist</code> function 
in the <code>pcds</code> package but is 
different from the <code><a href="/link/dist?package=nnspat&version=0.1.2" data-mini-rdoc="nnspat::dist">dist</a></code> function 
in the <code>stats</code> package of the standard R distribution.
<code>dist</code> requires its argument to be a data matrix and 
<code><a href="/link/dist?package=nnspat&version=0.1.2" data-mini-rdoc="nnspat::dist">dist</a></code> computes and returns 
he distance matrix computed by using the specified distance measure 
to compute the distances between the rows of a data matrix
(S-Book:1998;textualnnspat),
while <code>euc.dist</code> needs two arguments to find the distances between. 
For two data matrices <code>A</code> and <code>B</code>,
<code>dist(rbind(as.vector(A),as.vector(B)))</code> and 
<code>euc.dist(A,B)</code> yield the same result.

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

euc.dist function

<dl><dt>x, y</dt>
<dd>Vectors, matrices or data frames 
(both should be of the same type).</dd></dl>

Arguments

Author

Returns the Euclidean distance between <code>x</code> and <code>y</code> 
which can be vectors #' or matrices or data frames of
 any dimension (<code>x</code> and <code>y</code> should be of same dimension).
This function is equivalent to <code>Dist</code> function 
in the <code>pcds</code> package but is 
different from the <code><a href='https://rdrr.io/r/stats/dist.html'>dist</a></code> function 
in the <code>stats</code> package of the standard R distribution.
<code>dist</code> requires its argument to be a data matrix and 
<code><a href='https://rdrr.io/r/stats/dist.html'>dist</a></code> computes and returns 
he distance matrix computed by using the specified distance measure 
to compute the distances between the rows of a data matrix
(S-Book:1998;textualnnspat),
while <code>euc.dist</code> needs two arguments to find the distances between. 
For two data matrices <code>A</code> and <code>B</code>,
<code>dist(rbind(as.vector(A),as.vector(B)))</code> and 
<code>euc.dist(A,B)</code> yield the same result.

The Euclidean distance between two vectors, matrices, or data frames — euc.dist

<dl>

<dt>x, y</dt>
<dd>Vectors, matrices or data frames 
(both should be of the same type).</dd>

</dl>

euc.dist: The Euclidean distance between two vectors, matrices, or data frames

Description

Usage

Value

Arguments

Author

References

See Also

Examples