hsa_quant: Quantify the stability of hespdiv clusters

In hespdiv: Hierarchical Spatial Data Subdivision into Topologically Contiguous Units

Quantify the stability of hespdiv clusters

Description

This function evaluates the stability of the basal subdivision clusters using hespdiv sensitivity analysis results. It does so by calculating Jaccard similarities between the observations of basal subdivision clusters and the observations of alternative subdivision clusters. For each basal cluster, the function identifies the most similar analog cluster within each alternative subdivision. The stability of each basal cluster can be assessed by examining the distribution of similarity values with their corresponding analog clusters. If a highly similar cluster reappears in multiple alternative subdivisions, it indicates that the basal cluster is stable.

Usage

hsa_quant(obj, probs = c(0.05, 0.5, 0.95))

Arguments

obj	An object of class hsa.
probs	A numeric vector of probabilities with values in the range 0 \le p \le 1. This argument is used to calculate quantiles of Jaccard similarity values.

Details

If a base subdivision cluster obtains a distribution of high similarity values, it is considered stable and existing. Low analog-cluster similarity values may indicate that a base cluster is an artifact of the hespdiv() computation.

The more technical description of how hsa_quant works:

Obtaining alternative hespdiv clusters:

The function filters the xy.dat coordinates of the basal subdivision using all the polygons of alternative subdivisions, obtaining alternative hespdiv clusters.

Quantifying Jaccard similarity:

The function measures the Jaccard overlap index between the observations of the basal subdivision clusters and the observations of the alternative clusters.

Identification of analog clusters and value assignments:

Each basal hespdiv cluster from each alternative subdivision is assigned the ID of the cluster that produced the maximum Jaccard similarity value, along with the corresponding similarity value.

The purpose of the hsa_quant function is to address situations where hespdiv polygons, despite having different geometry and location, may filter nearly identical sets of observations, leading to similar hespdiv clusters. This can occur when the spatial coverage of observations is incomplete and irregular, or when the boundaries between hespdiv polygons are expected to be open, soft, or fuzzy, such as in the case of boundaries between bioregions. In such cases, visual hespdiv sensitivity analysis alone may show irregular and non-converging distributions of split-lines. However, hsa_quant can reveal that these irregular polygons are based on nearly identical clusters of observations, indicating a strong spatial structure within the analyzed data. Conversely, if the observations within these clusters significantly differ, it indicates that the basal clusters are specific to the hespdiv parameters used and likely lack ontological meaning.

Thus, by analyzing the similarity values between clusters of observations, hsa_quant facilitates the assessment of the stability and reliability of basal subdivision clusters, aiding in evaluating their significance.

Value

A list containing three data frames:

jaccard.quantiles

Quantiles of Jaccard similarities between the basal cluster and the analog clusters from alternative subdivisions.

jaccard.similarity

Jaccard similarity values between the basal cluster and the analog cluster from each alternative subdivision.

analog.clusters

IDs of the hespdiv polygons that produced the analog clusters in each alternative subdivision.

Note

You can use the hsa_quant function to track the evolution of hespdiv subdivisions over time by providing correctly formatted input. For instance, you can obtain the basal subdivision for time bin 1 using the hespdiv function. Then, using the hsa function, you can specify the paired xy.dat and data from time bin 2. The resulting hsa object can be inputted into hsa_quant The hsa_quant result will then provide insights into extinctions, speciations, fusions, and splits of hespdiv polygons/clusters that occur between time bin 1 and 2. This allows for the analysis of changes and dynamics in hespdiv subdivisions over time.

Author(s)

Liudas Daumantas

See Also

Other functions for hespdiv sensitivity analysis: change_base(), hsa(), hsa_detailed(), hsa_sample_constrained(), plot_cs_hsa(), plot_hsa(), plot_hsa_q()

Other functions to evaluate hesdpiv cluster stability: plot_hsa_q()

Other functions for hespdiv results post-processing: cross_comp(), hsa(), hsa_detailed(), hsa_sample_constrained(), nulltest(), taxon_effect()

hespdiv documentation built on May 21, 2026, 5:09 p.m.