predict_marks

Prefer using the S3 method <code><a href="/link/predict()?package=ldmppr&version=1.1.2" data-mini-rdoc="ldmppr::predict()">predict()</a></code> on an <code>ldmppr_mark_model</code>:
<code>predict(mark_model, sim_realization = ..., xy_bounds = ...)</code>.
This wrapper is retained for backward compatibility and is deprecated.

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

predict_marks function

<dl><dt>sim_realization</dt>
<dd>a data.frame containing a thinned or unthinned realization from
<code>simulate_mpp</code> (or <code>simulate_sc</code>). Must contain <code>x</code>, <code>y</code>, and <code>time</code>.</dd>
<dt>raster_list</dt>
<dd>list of raster objects used for mark prediction.</dd>
<dt>scaled_rasters</dt>
<dd><code>TRUE</code> or <code>FALSE</code> indicating whether rasters are already scaled.</dd>
<dt>mark_model</dt>
<dd>a mark model object. May be an <code>ldmppr_mark_model</code>, <code>model_fit</code>, or <code>workflow</code>.</dd>
<dt>xy_bounds</dt>
<dd>vector of domain bounds as <code>c(a_x, b_x, a_y, b_y)</code>.</dd>
<dt>include_comp_inds</dt>
<dd><code>TRUE</code> or <code>FALSE</code> indicating whether to generate and use competition indices as covariates.</dd>
<dt>competition_radius</dt>
<dd>positive numeric distance used when <code>include_comp_inds = TRUE</code>.</dd>
<dt>edge_correction</dt>
<dd>type of edge correction to apply (<code>"none"</code> or <code>"toroidal"</code>).</dd>
<dt>seed</dt>
<dd>optional nonnegative integer seed for reproducibility.</dd></dl>

Arguments

Prefer using the S3 method <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> on an <code>ldmppr_mark_model</code>:
<code>predict(mark_model, sim_realization = ..., xy_bounds = ...)</code>.
This wrapper is retained for backward compatibility and is deprecated.

Predict values from the mark distribution — predict_marks

<dl>

<dt>sim_realization</dt>
<dd>a data.frame containing a thinned or unthinned realization from
<code>simulate_mpp</code> (or <code>simulate_sc</code>). Must contain <code>x</code>, <code>y</code>, and <code>time</code>.</dd>


<dt>raster_list</dt>
<dd>list of raster objects used for mark prediction.</dd>


<dt>scaled_rasters</dt>
<dd><code>TRUE</code> or <code>FALSE</code> indicating whether rasters are already scaled.</dd>


<dt>mark_model</dt>
<dd>a mark model object. May be an <code>ldmppr_mark_model</code>, <code>model_fit</code>, or <code>workflow</code>.</dd>


<dt>xy_bounds</dt>
<dd>vector of domain bounds as <code>c(a_x, b_x, a_y, b_y)</code>.</dd>


<dt>include_comp_inds</dt>
<dd><code>TRUE</code> or <code>FALSE</code> indicating whether to generate and use competition indices as covariates.</dd>


<dt>competition_radius</dt>
<dd>positive numeric distance used when <code>include_comp_inds = TRUE</code>.</dd>


<dt>edge_correction</dt>
<dd>type of edge correction to apply (<code>"none"</code> or <code>"toroidal"</code>).</dd>


<dt>seed</dt>
<dd>optional nonnegative integer seed for reproducibility.</dd>

</dl>

predict_marks: Predict values from the mark distribution

Description

Usage

Value

Arguments