swmud

This dataset contains 177 samples of 3 variables including
longitude (long), latitude (lat), mud content (mud).

datasets

Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.

Jin Li

Spatial Predictive Modeling

swmud function

A data frame with 177 observations on the following 3 variables.<dl class="dl-horizontal">
 <dt><code>long</code></dt><dd>a numeric vector, decimal degree</dd>
 <dt><code>lat</code></dt><dd>a numeric vector, decimal degree</dd>
 <dt><code>mud</code></dt><dd>a numeric vector, percentage</dd>
 </dl>

Format

A dataset of seabed mud content in the southwest Australia Exclusive Economic Zone — swmud

A data frame with 177 observations on the following 3 variables.<dl class='dl-horizontal'>
 <dt><code>long</code></dt><dd>a numeric vector, decimal degree</dd>
 <dt><code>lat</code></dt><dd>a numeric vector, decimal degree</dd>
 <dt><code>mud</code></dt><dd>a numeric vector, percentage</dd>
 </dl>

swmud: A dataset of seabed mud content in the southwest Australia Exclusive Economic Zone

Description

Usage

Arguments

Format

Details