clust_sc: Score-based cluster analysis

In saskiaotto/INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States

Description

clust_sc computes a hierarchical cluster analysis for the identification of indicator redundancies.

Usage

clust_sc(dist_mat, method_clust = "average", ...)

Arguments

dist_mat	The distance matrix computed by the dist_sc function.
method_clust	The agglomeration method to be used in the hclust function. Default is "average", for alternatives see hclust.
...	Further arguments to be passed to the method hclust.

Value

An object of class hclust is returned, which describes the tree produced by the clustering process. See for more details hclust. Additionally, the cophenetic correlation coefficient and the Gower distance are printed in the console as guidance for selecting the best agglomeration method.

See Also

hclust

Other score-based IND performance functions: dist_sc_group(), dist_sc(), expect_resp(), plot_clust_sc(), plot_spiechart(), scoring(), summary_sc()

Examples

# Using the Baltic Sea demo data
scores_tbl <- scoring(trend_tbl = model_trend_ex,
  mod_tbl = all_results_ex, press_type = press_type_ex)
scores_mat <- summary_sc(scores_tbl)$scores_matrix
dist_matrix <- dist_sc(scores_mat, method_dist = "euclidean")
clust_analysis <- clust_sc(dist_matrix, method_clust = "complete")

saskiaotto/INDperform documentation built on Oct. 27, 2021, 10:33 p.m.