generate_grid

Input the limits of a 2D space and the desired image resolution, then
the function outputs the appropriate grid/lattice coordinates.

Provides tools for simulating spatially dependent predictors (continuous or binary),
which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
predictors are generated using traditional multivariate normal distributions or Gauss Markov random
fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors
exhibiting spatial clustering can also be easily specified by the user.

Justin Leach

sim2Dpredictr

Simulate Outcomes Using Spatially Dependent Design Matrices

generate_grid function

<dl><dt>im.res</dt>
<dd>A vector specifying the dimension/resolution of the image. The 
first entry is the number of 'rows' in the lattice/image, and the second 
entry is the number of columns' in the lattice/image.</dd>
<dt>xlim, ylim</dt>
<dd>These are the 2D image limits. Defaults for both are 
<code>c(0, 1)</code>. It is not recommended to alter these arguments unless 
changing the limits has a specific practical utility.</dd></dl>

Arguments

Convert a 2D Space to Grid Coordinates — generate_grid

<dl>

<dt>im.res</dt>
<dd>A vector specifying the dimension/resolution of the image. The 
first entry is the number of 'rows' in the lattice/image, and the second 
entry is the number of columns' in the lattice/image.</dd>


<dt>xlim, ylim</dt>
<dd>These are the 2D image limits. Defaults for both are 
<code>c(0, 1)</code>. It is not recommended to alter these arguments unless 
changing the limits has a specific practical utility.</dd>

</dl>

generate_grid: Convert a 2D Space to Grid Coordinates

Description

Usage

Value

Arguments