importance

importance.SCE

importance.SCA

Calculate variable importance for SCE or SCA models.

Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.

Kailong Li

Stepwise Clustered Ensemble

importance function

<dl> <dt>object</dt>
<dd>An SCE or SCA model object</dd> <dt>OOB_weight</dt>
<dd>Use out-of-bag weights for importance calculation (SCE only, default: TRUE)</dd> <dt>digits</dt>
<dd>Number of decimal places to round the returned relative importance values (default: 2)</dd> <dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Variable Importance for SCE and SCA Models — importance

<dl>

 <dt>object</dt>
<dd>An SCE or SCA model object</dd>

 <dt>OOB_weight</dt>
<dd>Use out-of-bag weights for importance calculation (SCE only, default: TRUE)</dd>

 <dt>digits</dt>
<dd>Number of decimal places to round the returned relative importance values (default: 2)</dd>

 <dt>...</dt>
<dd>Additional arguments</dd>

</dl>

importance: Variable Importance for SCE and SCA Models

Description

Usage

Value

Arguments

See Also