The dist_sc_group function computes a distance matrix for each group of scoring criteria and then averages the pair-wise distances across groups. This allows the computation of a more weighted distance measure and a closer clustering of indicators that e.g. respond to the same pressure types.

dist_sc_group(x, method_dist = "euclidean", ...)



A list of data frames or matrices that contain the indicator scores per group (see details). This could be the $scores_matrix output of the summary_sc, split into the different criteria groups.


Dissimilarity index used in the vegdist function to to calculate the dissimilarity matrix based on the scores. Default is `euclidean`, for alternatives see vegdist.


Further arguments to be passed to the method vegdist.


The function returns a dist object.


Ordinary distance measures such as the Euclidean, Bray Curtis or Canberra distance treat all variables, i.e. here the criteria, the same, which might be not always desirable. For instance, two indicators that show no trend but respond each to a specific type of fishing pressure (with a score of 1) and a third indicator that only shows a trend (score of 1 here) would have all the same distance to each other. So to add more weight to the similarity of the first two indicators responding to the same pressure type, this function computes separate distance matrices that are then averaged.

See also

summary_sc and vegdist for the computation of the dissimilarity index

Other score-based IND performance functions: clust_sc(), 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 # Split the scores by pressure-independent criteria and pressure types dist_matrix <- dist_sc_group(x = list( scores_mat[,1:2], scores_mat[,3:8], scores_mat[,9:12], scores_mat[,13:16]) )