pnp_kde

This function both fits Kernel Density Estimation (KDE) distributions and projects those distributions to new covariates..

internal

Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.

Brian S. Maitner

S4DM

Small Sample Size Species Distribution Modeling

Robert L. Richards

Ben S. Carlson

John M. Drake

Cory Merow

pnp_kde function

<dl><dt>data</dt>
<dd>dataframe of covariates</dd>
<dt>method</dt>
<dd>one of either "fit" or "predict"</dd>
<dt>bwmethod</dt>
<dd>Bandwidth method to use. One of 'normal-reference' (the default),'cv.ml', or 'cv.ls'</dd>
<dt>object</dt>
<dd>fitted object returned by a pnp_... function. Only needed when method = "predict"</dd></dl>

Arguments

Internal function for fitting KDE distributions in plug-and-play SDMs. — pnp_kde

<dl>

<dt>data</dt>
<dd>dataframe of covariates</dd>


<dt>method</dt>
<dd>one of either "fit" or "predict"</dd>


<dt>bwmethod</dt>
<dd>Bandwidth method to use. One of 'normal-reference' (the default),'cv.ml', or 'cv.ls'</dd>


<dt>object</dt>
<dd>fitted object returned by a pnp_... function. Only needed when method = "predict"</dd>

</dl>

pnp_kde: Internal function for fitting KDE distributions in plug-and-play SDMs.

Description

Usage

Arguments