sim_spatial_sc

Simulate the spatial component of the self-correcting model

internal

A suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern.
You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model.
'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools.
Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results.
Documentation for the package 'ldmppr' is available in the form of a vignette.

Lane Drew

ldmppr

Estimate and Simulate from Location Dependent Marked Point
Processes

Andee Kaplan

sim_spatial_sc function

<dl><dt>M_n</dt>
<dd>a vector of (x,y)-coordinates for largest point.</dd>
<dt>params</dt>
<dd>a vector of parameters (alpha_2, beta_2).</dd>
<dt>nsim_t</dt>
<dd>number of points to simulate.</dd>
<dt>xy_bounds</dt>
<dd>vector of lower and upper bounds for the domain (2 for x, 2 for y).</dd></dl>

Arguments

Simulate the spatial component of the self-correcting model — sim_spatial_sc

<dl>

<dt>M_n</dt>
<dd>a vector of (x,y)-coordinates for largest point.</dd>


<dt>params</dt>
<dd>a vector of parameters (alpha_2, beta_2).</dd>


<dt>nsim_t</dt>
<dd>number of points to simulate.</dd>


<dt>xy_bounds</dt>
<dd>vector of lower and upper bounds for the domain (2 for x, 2 for y).</dd>

</dl>

sim_spatial_sc: Simulate the spatial component of the self-correcting model

Description

Usage

Value

Arguments