clust_sc: Score-based cluster analysis
In INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States

Description Usage Arguments Value See Also Examples

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

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

`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`.

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.

hclust

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

# 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")