globalfood

A dataset containing four variables of 113 countries for their food security
index based on panelists evaluation in 2017.

datasets

Algorithms of distance-based k-medoids clustering: simple and fast
k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids.
Calculate distances for mixed variable data such as Gower, Podani, Wishart,
Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and
relative criteria. The internal criteria includes silhouette index and shadow
values. The relative criterium applies bootstrap procedure producing a heatmap
with a flexible reordering matrix algorithm such as complete, ward, or average
linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Weksi Budiaji

kmed

Distance-Based k-Medoids

globalfood function

A data frame with 113 rows and 4 variables:<dl>
 <dt>affordability</dt>
<dd>Index of food affordability.</dd> <dt>availability</dt>
<dd>Index of food availability.</dd> <dt>safety</dt>
<dd>Index of food quality and safety.</dd> <dt>resilience</dt>
<dd>Index of natural resources and resilience.</dd>
</dl>

Format

Global food security index — globalfood

A data frame with 113 rows and 4 variables:<dl>
 <dt>affordability</dt>
<dd>Index of food affordability.</dd>

 <dt>availability</dt>
<dd>Index of food availability.</dd>

 <dt>safety</dt>
<dd>Index of food quality and safety.</dd>

 <dt>resilience</dt>
<dd>Index of natural resources and resilience.</dd>


</dl>

globalfood: Global food security index

Description

Usage

Arguments

Format