Apply geometrical transformations
 to a linear network.

affine.linnet

Defines types of spatial data on a linear network
and provides functionality for geometrical operations,
data analysis and modelling of data on a linear network,
in the 'spatstat' family of packages.
Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
Data types defined on a network include point patterns, pixel images, functions, and tessellations.
Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Random point patterns on a network can be generated using a variety of models.

Adrian Baddeley

spatstat.linnet

Linear Networks Functionality of the 'spatstat' Family

Rolf Turner

Ege Rubak

Greg McSwiggan

Tilman Davies

Mehdi Moradi

Suman Rakshit

Ottmar Cronie

affine.linnet function

Apply Geometrical Transformations to a Linear Network — affine.linnet

<dl>

 <dt>X</dt>
<dd>Linear network (object of class <code>"linnet"</code>).</dd>

 <dt>mat</dt>
<dd>Matrix representing a linear transformation.</dd>

 <dt>vec</dt>
<dd>Vector of length 2 representing a translation.</dd>

 <dt>angle</dt>
<dd>Rotation angle in radians.</dd>

 <dt>f</dt>
<dd>Scalar dilation factor.</dd>

 <dt>s</dt>
<dd>Unit conversion factor: the new units are <code>s</code> times the old units.</dd>

 <dt>...</dt>
<dd>Arguments passed to other methods.</dd>

 <dt>origin</dt>
<dd>Character string determining a location
 that will be shifted to the origin. Options are
 <code>"centroid"</code>, <code>"midpoint"</code> and <code>"bottomleft"</code>.
 Partially matched.</dd>

 <dt>centre</dt>
<dd>Centre of rotation.
 Either a vector of length 2, or a character string
 (partially matched to <code>"centroid"</code>, <code>"midpoint"</code>
 or <code>"bottomleft"</code>).
 The default is the coordinate origin <code>c(0,0)</code>.</dd>

 <dt>unitname</dt>
<dd>Optional. New name for the unit of length.
 A value acceptable to the function <code>unitname&lt;-</code></dd>

</dl>

Arguments

Adrian Baddeley <a href='mailto:Adrian.Baddeley@curtin.edu.au'>Adrian.Baddeley@curtin.edu.au</a> 
 
and Rolf Turner <a href='mailto:rolfturner@posteo.net'>rolfturner@posteo.net</a>

Author

Apply Geometrical Transformations to a Linear Network

affine.linnet: Apply Geometrical Transformations to a Linear Network

Description

Usage

Arguments

Value

Details

See Also

Examples