SOMevidence

SOMevidence is an R package for auditing whether classifications derived from self-organizing maps (SOMs) meet prespecified evidence requirements under an explicit analysis design. It is intended for ecological, environmental, and evolutionary data in which sampling groups, monitoring domains, repeated observations, multiple data layers, and incomplete transferability are common.

The package uses kohonen as its SOM training backend. Its contribution is an evidence workflow around SOM training: explicit data design, leakage-safe preprocessing, controlled ensembles, partition stability, cross-model agreement, transfer diagnostics, and conditional decisions that can abstain when evidence is insufficient. The validation scope distinguishes software checks, design-conditioned reproducibility, worked examples, and external validation.

What the package separates

• representation quality from hard-partition stability;
• analysis-scope reproducibility from held-domain transfer;
• cross-model agreement from ecological validation;
• post hoc agreement with external labels from classification accuracy; and
• evidence reporting from analyst-defined decision thresholds.

This separation is deliberate. SOMevidence does not select a universal best map, force ecological class names, or turn unsupervised agreement into evidence of ecological truth.

Main capabilities

• leakage-safe preprocessing fitted separately within each analysis split;
• row-, group-, block-, and domain-aware resampling designs;
• reproducible ensembles across map grids, random seeds, and resamples;
• exact cross-fit audits of continuous SOM topology under explicit computation budgets;
• quantization, topology, occupancy, consensus, entropy, Jaccard, ARI, and AMI diagnostics;
• controlled triangulation with K-means, Ward.D2, and, when requested, Gaussian mixture models;
• held-domain and later-batch mapping without refitting preprocessing on the assessment data;
• explicit analyst-specified evidence gates with an "abstain" outcome; and
• analytical graphics plus an optional Shiny interface that exports R code and configuration.

Metrics dictionary

Directions below are diagnostic tendencies within otherwise comparable data, preprocessing, scopes, and model budgets. They are not universal thresholds and should not be combined into an automatic leaderboard.

Question	Metrics	Diagnostic direction	Required evidence	Does not establish
How well does a fitted map represent its analysis data?	Quantization error, topographic error, empty-unit rate	Generally lower within comparable fits; inspect trade-offs	Successful fits using the same variables, preprocessing, scope, and comparable grids	A best map, a true number of classes, or ecological validity
Is the continuous SOM topology reproducible across fitted maps?	Rank correlation of shortest-hop sample-pair distances; optional tie-preserving neighbourhood Jaccard	Higher concordance under the prespecified fit-pair design; inspect coverage and unevaluable cases	Common sample IDs, comparable scopes, successful mappings, and an explicit exact-computation budget	Interchangeable codebooks, a best map, discrete classes, or independent inferential replication
Is a fixed-k partition reproducible across the planned ensemble?	Pairwise ARI/AMI, clusterwise Jaccard, joint coverage, complete-partition rate	Agreement and coverage higher; every source partition retains k	At least two successful, comparable partitions with adequate shared observations	That discrete types exist, that k is optimal, or that labels are correct
How consistently is each sample assigned?	Membership support, assignment entropy, assignment, consensus-label, and replicated coverage	Support and coverage higher; entropy lower	An identifiable consensus with repeated assignments and explicit coverage	A posterior probability or probability that an assignment is correct
Do controlled reference algorithms agree with the SOM partition?	Method-specific SOM-to-reference ARI, method count, reference-fit success	Agreement higher, with all requested methods evaluable	Prespecified reference methods fitted on common splits and preprocessing	Accuracy, independence of evidence, or ecological validation
Does the representation map comparably to a held-out domain?	Assessment-to-analysis distance ratio, unoccupied-unit rate, mapping coverage	Ratio nearer 1, unoccupied rate lower, coverage higher; inspect all fits	Genuine held-domain rows mapped using training-derived preprocessing	Predictive accuracy, causal explanation of shift, or general transferability
Are conclusions sensitive to prespecified analysis choices?	Scenario ARI/AMI, shared- and all-members Jaccard, contrast coverage	Agreement and coverage higher across the stated scenarios	Stable sample IDs, prespecified scenarios, and adequate shared observations	Robustness to unexamined choices or a probability that membership is correct
Does a consensus agree with labels excluded from training?	External-label ARI/AMI, contingency and composition tables	Agreement higher among evaluable samples	Labels kept outside training and joined by stable sample identity	Classification accuracy, ecological ground truth, or causation
Did a candidate meet the analyst’s evidence requirements?	supported, abstain, uncertain, and the individual gate checks	All specified checks pass; failures abstain; unavailable evidence is uncertain	Prespecified thresholds and complete evidence from the same workflow	The existence of discrete ecological types or universal scientific defensibility

SOMevidence calculates AMI with chance adjustment from the expected mutual information and uses the arithmetic mean of the two partition entropies as its normalizer. Software that uses a different AMI normalization can return a different numerical value for the same pair of partitions.

Representation-level checks alone cannot support a hard partition. A supported decision requires traceable comparative partition evidence and at least one prespecified partition-quality requirement. Otherwise the missing requirement is reported as unavailable and assess_defensibility() returns "uncertain".

Installation

Install the current release from R-universe:

options(repos = c(
  shaowen_ye = "https://shaowen-ye.r-universe.dev",
  CRAN = "https://cloud.r-project.org"
))
install.packages("SOMevidence")

Install the source currently on GitHub with pak:

# install.packages("pak")
pak::pak("shaowen-ye/SOMevidence")

Quick start

The following small run varies the resample, map grid, and random start. Its settings are deliberately modest for an interactive example; a scientific analysis should justify a larger, prespecified model budget.

library(SOMevidence)

dat <- simulate_som_scenario("clusters", n = 120, p = 6, seed = 1)
splits <- som_resamples(
  dat,
  method = "subsample",
  repeats = 5,
  prop = 0.9,
  seed = 2
)
spec <- som_spec(
  grids = list(c(4, 3), c(5, 4)),
  seeds = c(3, 4),
  rlen = 50,
  k = 2:4
)

result <- run_som_workflow(
  dat,
  spec,
  splits,
  cross_models = c("kmeans", "ward")
)

result$audit$grid_summary
result$partitions$stability
result$consensus$k3$cluster_summary
result$cross_comparison$summary
summary(result)
result$consensus_failures

# Compare one prespecified pair of successful SOM representations.
successful_ids <- vapply(
  Filter(function(fit) isTRUE(fit$success), result$ensemble$fits),
  `[[`, character(1), "id"
)
representation <- audit_som_representation(
  result$ensemble,
  pairs = data.frame(
    fit_a = successful_ids[[1]],
    fit_b = successful_ids[[2]]
  ),
  neighbourhood_size = 10
)
representation$pairwise

The workflow reports every requested candidate k; it does not silently pick one. Gaussian mixture-model comparison is available when the suggested package mclust is installed and "gmm" is requested explicitly. audit_som_representation() is experimental and descriptive: it does not score, rank or select maps, and its fit pairs and sample pairs are not treated as independent replicates.

Start from your own data

Separate predictors from sampling-design fields before constructing the analysis object. Numeric site codes, coordinates, dates, depths, and external class labels should not enter the SOM merely because they are numeric. The placeholder path below belongs in a separate, controlled analysis project; do not copy confidential study data into the package source repository.

raw <- read.csv("data/your-analysis-ready-data.csv", check.names = FALSE)
predictors <- c("temperature", "oxygen", "conductivity", "total_nitrogen")

dat <- som_data(
  x = raw[predictors],
  id = raw$sample_id,
  group = raw$site_id,
  time = raw$sampling_date,
  domain = raw$survey_campaign
)

# Illustrative settings only: choose these from the sampling design and a
# prespecified computation budget for the study at hand.
splits <- som_resamples(
  dat,
  method = "group_subsample",
  repeats = 20,
  prop = 0.8,
  seed = 2026
)
spec <- som_spec(
  grids = list(c(7, 5), c(8, 6)),
  seeds = 1:10,
  rlen = 1000,
  k = 2:8
)
result <- run_som_workflow(
  dat,
  spec,
  splits,
  cross_models = c("kmeans", "ward")
)
summary(result)

Choose resampling from the observation hierarchy, not from convenience:

Data structure	Starting design
Independent observations	subsample
Repeated observations within sites or subjects	group_subsample
Prespecified spatial or temporal blocks	block_subsample
Transfer across campaigns, regions, or instruments	leave_domain_out
A protocol-defined split	custom

This table is a starting point rather than an automatic rule. Dependence, sample size, domain coverage, transformations, missingness, and the scientific question still require study-specific decisions. Inspect the workflow summary, all failure and warning tables, and the separate evidence streams before interpreting any partition.

Optional interactive interface

launch_som_app() returns a shiny.appobj, which can be printed in an interactive R session or passed to shiny::runApp(). SOMevidence adds no telemetry. When the app runs in a local R session, a selected CSV remains on the local computer. If the app is deployed on a remote Shiny host, selecting a file transfers it to that host; users should follow the host operator’s access and data-handling controls. The application helps configure and inspect a workflow, but it does not return an analysis object. The YAML export is a configuration snapshot. The exported R script can be rerun, but neither export replaces the exact input data, package versions, warnings, failures, and results required for a reproducible analysis record.

Learn with real and simulated data

The package website contains five executable tutorials:

• the core ensemble and evidence workflow;
• API lifecycle and reproducibility contracts;
• design-aware resampling and held-domain transfer;
• evidence visualization; and
• a real-data analysis of Palmer penguin morphology, with species labels held out until post hoc agreement assessment.

open_data_registry() lists the implemented tutorial source and additional curated candidate datasets for ecological, environmental, and evolutionary examples. Candidate datasets are not claimed to be package validation data and are never downloaded during installation or package checks.

Reproducible reporting

For a scientific analysis, retain at least:

• the SOMevidence version and sessionInfo();
• the analysis-ready data provenance and stable sample identifiers;
• the preprocessing, resampling, SOM, and cross-model specifications;
• all seeds, model warnings, model failures, and decision-gate settings; and
• the exact script that produced the reported tables and figures.

Persisted public som_* objects carry a structural contract version. upgrade_som_object() can make only deterministic migrations supported by retained evidence; it asks for recomputation when a scientifically important field cannot be recovered. The object contract is not a substitute for the package version, input provenance or sessionInfo().

Package-level checks reduce software errors, but they do not establish that a sampling design, candidate k, ecological interpretation, or threshold is appropriate for a particular study.

Citation, support, and contribution

Use citation("SOMevidence") for the current citation. Questions and bug reports are handled through GitHub Issues; please read SUPPORT.md before filing a report. Contributions are welcome under the process in CONTRIBUTING.md.

License

SOMevidence is distributed under the GNU General Public License version 3 or later. See LICENSE.md.