ci_rbod_spatial

The Spatial robust Benefit of the Doubt approach (Sp-RBoD) method allows to take into account the spatial contextual condition into the robust Benefit of the Doubt method.

A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.

Francesco Vidoli

Compind

Composite Indicators Functions

Elisa Fusco

ci_rbod_spatial function

<dl> <dt>x</dt>
<dd>A data.frame containing score of the simple indicators.</dd> <dt>indic_col</dt>
<dd>Simple indicators column number.</dd> <dt>M</dt>
<dd>The number of elements in each of the bootstrapped samples; default is 20.</dd> <dt>B</dt>
<dd>The number of bootstrap replicates; default is 100.</dd> <dt>W</dt>
<dd>The spatial weights matrix. A square non-negative matrix with no NAs representing spatial weights; may be a matrix of class "sparseMatrix" (spdep package)</dd> 
</dl>

Arguments

Author

Spatial robust Benefit of the Doubt approach (Sp-RBoD) — ci_rbod_spatial

<dl>

 <dt>x</dt>
<dd>A data.frame containing score of the simple indicators.</dd>

 <dt>indic_col</dt>
<dd>Simple indicators column number.</dd>

 <dt>M</dt>
<dd>The number of elements in each of the bootstrapped samples; default is 20.</dd>

 <dt>B</dt>
<dd>The number of bootstrap replicates; default is 100.</dd>

 <dt>W</dt>
<dd>The spatial weights matrix. A square non-negative matrix with no NAs representing spatial weights; may be a matrix of class "sparseMatrix" (spdep package)</dd>

 
</dl>

ci_rbod_spatial: Spatial robust Benefit of the Doubt approach (Sp-RBoD)

Description

Usage

Value

Arguments

Author

References

See Also

Examples