datasplit

This function is a data splitting function for k-fold cross-
validation and uses a stratified random sampling technique. It resamples the
training data based on sample quantiles.

An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Jin Li

spm2

Spatial Predictive Modeling

datasplit function

<dl><dt>trainy</dt>
<dd>a vector of response, must have a length equal to sample size.</dd>
<dt>k.fold</dt>
<dd>integer; number of folds in the cross-validation. if &gt; 1,
then apply k-fold cross validation; the default is 10, i.e., 10-fold cross
validation that is recommended.</dd></dl>

Arguments

Author

Split data for k-fold cross-validation — datasplit

<dl>

<dt>trainy</dt>
<dd>a vector of response, must have a length equal to sample size.</dd>


<dt>k.fold</dt>
<dd>integer; number of folds in the cross-validation. if &gt; 1,
then apply k-fold cross validation; the default is 10, i.e., 10-fold cross
validation that is recommended.</dd>

</dl>

datasplit: Split data for k-fold cross-validation

Description

Usage

Value

Arguments

Author

References

Examples