summary.BMEmapping

Provides a concise summary of LOOCV performance for BME predictions at hard
data locations. Computes standard residual-based accuracy metrics, including
Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).

Provides an accessible and robust implementation of core BME
methodologies for spatial prediction. It enables the systematic integration
of heterogeneous data sources including both hard data (precise
measurements) and soft interval data (bounded or uncertain observations)
while incorporating prior knowledge and supporting variogram-based spatial
modeling. The BME methodology is described in Christakos (1990)
<doi:10.1007/BF00890661> and Serre and Christakos (1999)
<doi:10.1007/s004770050029>.

Kinspride Duah

BMEmapping

Spatial Interpolation using Bayesian Maximum Entropy (BME)

Yan Sun

summary.BMEmapping function

<dl><dt>object</dt>
<dd>An object of class <code>"bme_cv"</code>, typically returned by a
BME cross-validation function. Must contain a numeric vector named
<code>residual</code>.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/summary()?package=BMEmapping&version=1.2.2" data-mini-rdoc="BMEmapping::summary()">summary()</a></code>.</dd></dl>

Arguments

Summary Method for BME Cross-Validation Results — summary.BMEmapping

<dl>

<dt>object</dt>
<dd>An object of class <code>"bme_cv"</code>, typically returned by a
BME cross-validation function. Must contain a numeric vector named
<code>residual</code>.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/base/summary.html'>summary()</a></code>.</dd>

</dl>

summary.BMEmapping: Summary Method for BME Cross-Validation Results

Description

Usage

Value

Arguments