full_sc_lhood

calculates full self-correcting log-likelihood

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

full_sc_lhood function

<dl><dt>xgrid</dt>
<dd>a vector of grid values for x.</dd>
<dt>ygrid</dt>
<dd>a vector of grid values for y.</dd>
<dt>tgrid</dt>
<dd>a vector of grid values for t.</dd>
<dt>tobs</dt>
<dd>a vector of observed values for t.</dd>
<dt>data</dt>
<dd>a matrix of times and locations.</dd>
<dt>params</dt>
<dd>a vector of parameters.</dd>
<dt>bounds</dt>
<dd>a vector of bounds for time, x, and y.</dd></dl>

Arguments

calculates full self-correcting log-likelihood — full_sc_lhood

<dl>

<dt>xgrid</dt>
<dd>a vector of grid values for x.</dd>


<dt>ygrid</dt>
<dd>a vector of grid values for y.</dd>


<dt>tgrid</dt>
<dd>a vector of grid values for t.</dd>


<dt>tobs</dt>
<dd>a vector of observed values for t.</dd>


<dt>data</dt>
<dd>a matrix of times and locations.</dd>


<dt>params</dt>
<dd>a vector of parameters.</dd>


<dt>bounds</dt>
<dd>a vector of bounds for time, x, and y.</dd>

</dl>

full_sc_lhood: calculates full self-correcting log-likelihood

Description

Usage

Value

Arguments