Skip to contents

SOM + latent profile clustering pipeline (with AHP and distance baselines)

Source: R/Pipeline_SOMClust.R
CreateClusterModel_SOM_MClust.Rd

End-to-end pipeline to:

  • Standardize variables using SciDataReportR::CreateZScoreObject() or a supplied Z-score object.

  • Fit a Self-Organizing Map (SOM; kohonen) on complete cases.

  • Generate aweSOM visualizations (Circular, Line, Cloud) with optional relabeling using variable labels from the original data frame.

  • Cluster SOM codebook vectors using latent profile analysis (tidyLPA / mclust backend).

  • In method = "exploratory", fit a grid of models and select a recommended solution using an Analytic Hierarchy Process (AHP)-style index combining AIC, BIC, and Entropy.

  • In method = "finalize", fit a user-specified model and number of profiles.

  • Map node-level clusters and posterior probabilities back to individuals.

  • Store training variable summaries used later to quantify whether projected cohorts fall outside the original training range.

This supports a train once, project many clinical phenotyping workflow: learn phenotype structure in a training cohort, then project new cohorts into the fixed phenotype space without reclustering.

Ideal use: correlated continuous clinical or biomarker measures where a topology-preserving map is clinically informative before model-based profiles.

Missing data:

  • SOM and clustering are fit only on rows with complete Z-scores.

  • The returned DataWithClusters has exactly the original rows and columns plus one cluster column; rows not used in SOM/LPA get NA.

  • The returned ProbFit$individual is also full length, preserving one row per input row with NA posterior probabilities for rows excluded from SOM/LPA.

Z-score behavior:

  • ZScoreType = "Center and Scale"/"Center Only"/"Scale Only" computes Z-scores from df via CreateZScoreObject().

  • ZScoreType = "ZScoreObj" projects Z-scores using an external ZScoreObj via ProjectZScore().

  • ZScoreType = "PreZScored" uses existing Z-score columns in df as-is and does not re-zscore.

Readable SOM + Mclust workflow wrapper for CreateClusterModel_SOM_MClust().

Compatibility wrapper for Pipeline_SOM_MClust().

Deprecated alias for CreateClusterModel_SOM_MClust().

Usage

CreateClusterModel_SOM_MClust(
  data,
  variables = NULL,
  method = c("exploratory", "finalize", "explore"),
  k_range = 2:10,
  models = c(1, 2, 3),
  final_k = NULL,
  final_model = NULL,
  ClusterVariableName = "Cluster",
  ZScoreType = c("Center and Scale", "Center Only", "Scale Only", "ZScoreObj",
    "PreZScored"),
  ZScoreObject = NULL,
  som_xdim = NULL,
  som_ydim = NULL,
  som_topo = "hexagonal",
  som_neigh = "gaussian",
  seed_som = 934521L,
  seed_lpa = 93421L,
  Relabel = TRUE,
  ZScorePrefix = "Z_",
  ZScoreVars = NULL,
  id_var = NULL,
  lpa_progress = FALSE,
  lpa_em_itmax = 100L,
  lpa_em_tol = 1e-05,
  lpa_timeout_seconds = 120,
  lpa_drop_zero_sd = TRUE,
  lpa_zero_sd_tol = 1e-08,
  skip_model_after_n_failures = 2L,
  slow_fit_seconds = 120,
  min_nodes_per_cluster = 5,
  high_dist_quantile = 0.95,
  low_prob_threshold = 0.7,
  stability_resamples = 0L,
  stability_seed = 934522L,
  stability_progress = FALSE,
  df = lifecycle::deprecated(),
  id_col = lifecycle::deprecated(),
  .NodeClusterFn = NULL
)

Pipeline_SOM_MClust(...)

Pipeline_SOMClust(...)

CreateSOMClusterModel(...)

Arguments

data

Data frame containing the variables to be used in SOM and clustering.

variables

Optional character vector of variable names. If NULL, numeric variables are auto-detected using SciDataReportR::getNumVars(df, Ordinal = FALSE). In ZScoreType = "PreZScored", this can also be NULL if you supply ZScoreVars or if Z-score columns can be auto-detected by prefix.

method

One of "exploratory" (default) or "finalize". In "exploratory", a grid of models is fit and AHP chooses the recommended solution. In "finalize", the user must specify final_k and final_model.

k_range

Integer vector of numbers of clusters/profiles to consider in exploratory mode. Default 2:10.

models

Integer vector of model specifications for tidyLPA (mclust backend). Default c(1, 2, 3).

final_k

Integer; number of profiles for method = "finalize".

final_model

Integer; model specification for method = "finalize" (should be one of models).

ClusterVariableName

Name of the cluster column in the output. Defaults to "Cluster". If this column already exists in df, it is overwritten (with a message).

ZScoreType

One of:

  • "Center and Scale" (default)

  • "Center Only"

  • "Scale Only"

  • "ZScoreObj" (use an existing ZScore object)

  • "PreZScored" (use existing Z-score columns in df as-is)

ZScoreObject

Optional ZScoreObj (from CreateZScoreObject() or ProjectZScore()) to use when ZScoreType = "ZScoreObj".

som_xdim, som_ydim

Optional integers for SOM grid dimensions. If NULL, a square grid with side length ceiling(n_complete^(1/3)) is used.

som_topo

SOM topology for kohonen::somgrid(), default "hexagonal".

som_neigh

SOM neighbourhood function, default "gaussian".

seed_som, seed_lpa

Integer seeds for SOM and LPA steps (defaults 934521 and 93421).

Relabel

Logical; if TRUE (default), aweSOM plots are relabeled using variable labels from the original df (via Hmisc or sjlabelled when available) by stripping the Z-score prefix.

ZScorePrefix

Character prefix used for Z-score columns when ZScoreType = "PreZScored". Default "Z_".

ZScoreVars

Optional character vector of Z-score column names to use when ZScoreType = "PreZScored". If NULL, the function attempts to infer them from variables or by detecting columns starting with ZScorePrefix.

id_var

Optional character scalar. If provided and present in df, this column is carried into ProbFit$individual for convenience.

lpa_progress

Logical; if TRUE, print short progress messages while fitting model/profile combinations.

lpa_em_itmax

Integer; maximum number of EM iterations passed to mclust::emControl(). Use NULL to leave mclust defaults unchanged.

lpa_em_tol

Numeric; EM convergence tolerance passed to mclust::emControl(). Use NULL to leave mclust defaults unchanged.

lpa_timeout_seconds

Optional timeout in seconds for individual LPA fits. Use NULL to disable timeouts.

lpa_drop_zero_sd

Logical; if TRUE, remove SOM code dimensions with near-zero standard deviation before LPA.

lpa_zero_sd_tol

Numeric tolerance used when lpa_drop_zero_sd = TRUE.

skip_model_after_n_failures

Optional integer; skip a model family after this many failures.

slow_fit_seconds

Optional runtime threshold used to flag slow LPA fits in diagnostics.

min_nodes_per_cluster

Optional minimum average SOM nodes per cluster considered before attempting a candidate profile count.

high_dist_quantile

Numeric value between 0 and 1 used to define high SOM-distance flags from the training distance distribution. Default is 0.95.

low_prob_threshold

Numeric posterior probability threshold used to flag uncertain phenotype membership. Default is 0.70.

stability_resamples

Number of 80% participant subsample refits used to assess reproducibility for every successful exploratory candidate. Subsamples are drawn without replacement. Defaults to 0 (disabled); use 50 for an exploratory stability screen.

stability_seed

Integer seed for participant subsampling.

stability_progress

Logical; if TRUE, print subsample progress messages.

df

Deprecated (since 19.15.0). Use data instead.

id_col

Deprecated (since 19.15.0). Use id_var instead.

.NodeClusterFn

Internal. A function taking the SOM codebook matrix and returning a list with a node_cluster integer vector (one label per SOM node) and, optionally, fit_table, ahp_best_row, recommendation, best_fit_name, and fit_plot. When supplied, the SOM codebook is clustered by that function and the latent-profile grid is not fitted. Used by CreateClusterModel_SOM_HDBSCAN(); not part of the user-facing API.

...

Arguments passed to CreateClusterModel_SOM_MClust().

Value

A list of class "Pipeline_SOM_MClust" with components:

  • method, vars_used, ZScoreType, ZScoreObject, ZScoreVars, ClusterVariableName

  • DataWithClusters: original df with only the cluster column appended

  • fit_plot: ggplot of AIC/BIC/Entropy/BLRT p-value vs k and model (plus reproducibility when subsample stability is enabled)

  • ModelInfo_SOM: list with som_model, som_codes, som_grid, training_variable_summary, SOMFit (distance diagnostics, baselines, and per-cluster flags), plots (aweSOM plots)

  • ModelInfo_MClust: list with lpa_models, fit_table, AHP information, and diagnostics for LPA warnings, failures, runtimes, and preprocessing

  • ModelInfo_MClust$Stability: subsample replicate, cluster recovery, and summary tables when stability_resamples > 0

  • ProbFit: list with node (node-level posterior probabilities), individual (full-length per-person mapping and probabilities), and probability plots

Details

ModelInfo_MClust$fit_table uses the fit indices returned by tidyLPA's mclust backend. AIC, AWE, BIC, CAIC, CLC, KIC, SABIC, and ICL are likelihood/information criteria, for which lower values are preferred when comparing candidate fits estimated on the same data. Entropy summarizes classification separation (higher is better). MinProfileNodeN and MaxProfileNodeN are integer SOM-node counts in the smallest and largest profile; the corresponding *Proportion fields divide those counts by the total number of SOM nodes. BLRTStatistic and BLRTPValue compare a k-profile model with k - 1 profiles under the same covariance model. A small p-value supports the added profile's statistical fit, but does not establish clinical meaning or reproducibility; it is unavailable for k = 1. The raw BLRTPValue remains in the table. Its candidate-review panel displays -log10(BLRTPValue) with a dashed 0.05 p-value reference at -log10(0.05).

Lifecycle settings are intentionally strict: k_range and models are exploratory-only, while final_k and final_model are finalize-only. Supplying settings from both modes is an error, even when an exploratory setting equals its default.

Stability is assessed by refitting the full SOM/LPA pipeline on independent 80% participant subsamples drawn without replacement. StabilityARI_Mean is the mean adjusted Rand index across successful refits and can be negative when agreement is worse than chance. StabilityJaccard_Mean is the mean label-matched, per-profile Jaccard recovery. ReproducibilityScore is the mean of their finite values; StabilitySuccessRate is reported separately. Reproducibility_scaled is the candidate-table min-max rescaling used only by the AHP index and is not an independently interpretable reproducibility measure.

The AHP-style index is computed by:

  1. Scaling AIC, BIC, and Entropy across candidate solutions (AIC/BIC are negated so that lower values correspond to better fit; higher scaled scores are preferred).

  2. Taking the mean of the three scaled indices. The model with the highest AHP index is recommended.

LPA model/profile combinations are fit one at a time so that failed or warning-producing solutions are captured in diagnostics instead of blocking the entire pipeline. Successful fits are retained and failed fits are listed in ModelInfo_MClust$diagnostics.

Model review and refinement

Start with ModelInfo_MClust$fit_table, the AHP recommendation, and fit_plot to compare candidate model specifications and numbers of profiles. AHP is a useful starting point, not an automatic final decision: retain solutions whose fit, cluster size, and clinical interpretability are all reasonable.

The Circular and Line SOM widgets show how the analysis variables vary across the map; the Cloud widget shows the observations within map cells. Use them to assess whether the candidate solution represents coherent, interpretable phenotype patterns. Inspect SOMFit$node_occupancy for empty or sparse cells, and the SOM-distance plots for clusters with systematically poor map representation. The posterior-probability plots in ProbFit$plots identify uncertain node- or individual-level assignments.

If these diagnostics suggest a weak solution, reconsider the variables, SOM grid dimensions, model family, or candidate k range and refit in exploratory mode. Once a solution is selected, refit it with method = "finalize", final_k, and final_model to create the reusable model for projection.

Set stability_resamples to a positive value to assess internal reproducibility. Each 80% subsample refits the same candidate SOM/LPA solution and projects the original participants back into it. Mean adjusted Rand index summarizes whole-partition agreement; cluster-wise Jaccard recovery identifies phenotypes that dissolve despite good overall agreement. This is internal reproducibility, not independent-cohort validation.

Reviewing the candidates

Exploratory mode fits every combination of k and model family, so the comparison is made once, on one table, rather than by refitting by hand. ModelInfo_MClust$fit_table holds every candidate solution with its information criteria, entropy, subsample reproducibility, and the combined AHP index side by side.

A candidate missing from that table is never a mystery: ModelInfo_MClust$diagnostics$lpa_fit_diagnostics records each fit's status, runtime, warnings, and errors, so model families that fail to converge or exceed the fit timeout show up there.

Once a solution is chosen, refit it on its own with method = "finalize" to get the reusable, projectable model.

Stability output

Stability assesses internal reproducibility by full-pipeline 80% participant subsampling without replacement. For each replicate, 80% of complete participants are selected once, all preprocessing and any reduction (PCA, MCA, or SOM) are refit, the selected clustering method is refit, and the original complete training participants are projected into that subsample fit for comparison with the original fitted partition. It is an internal sensitivity analysis, not independent-cohort validation.

Stability$settings records the analysis provenance:

  • resamples: requested number of 80% subsample refits.

  • seed: seed used to select subsamples.

  • refit_scope: always "full_pipeline", meaning preprocessing, reduction where applicable, and clustering were all refit.

  • resample_type: "subsample_without_replacement" for the primary stability analysis.

  • resample_fraction: the retained participant fraction, 0.80.

  • coassignment_limit: maximum number of complete training participants (2,000) for which the full pairwise co-assignment matrix is calculated.

  • noise_policy: whether the method has noise labels. For ordinary methods it is "all clusters included"; HDBSCAN variants retain noise in global partition metrics but exclude it from per-cluster inclusion and co-assignment summaries.

Stability$replicates has one row per requested refit. Model and Classes identify the selected candidate (for HDBSCAN, Classes is the data-derived extracted count); Replicate is its sequence number; Status is "success" or a failure status; and Error contains the error message for an unsuccessful refit. Successful rows contain these partition metrics:

  • ARI: adjusted Rand index, agreement corrected for chance; higher is better and can be negative when agreement is worse than chance.

  • VI: variation of information, the information lost or gained when changing partitions; lower is better and zero is identical.

  • NMI: normalized mutual information; higher is better and one is identical.

  • FowlkesMallows: pairwise clustering agreement; higher is better and one is identical.

Stability$cluster_recovery has one row for each reference Cluster in each successful Replicate. Jaccard is the recovery of that reference cluster after matching it to the subsample cluster with the largest Jaccard overlap; higher is better and one is exact recovery. Model and Classes again identify the fitted candidate.

Stability$summary combines successful subsample refits: StabilitySuccessRate is successful refits divided by requested refits, an operational reliability measure that does not enter the reproducibility score. StabilityARI_Mean and StabilityARI_P05 are the mean and fifth percentile of ARI. StabilityJaccard_Mean and StabilityJaccard_Min are respectively the mean and minimum label-matched Jaccard recovery. ReproducibilityScore is the mean of the finite StabilityARI_Mean and StabilityJaccard_Mean values only; it does not include success rate, VI, NMI, or Fowlkes–Mallows.

Stability$failures repeats the replicate columns for unsuccessful refits, making fit failures auditable without mixing them with successful metrics.

Stability$participant_inclusion is one row per complete reference participant. RowIndex identifies its original row position, Cluster its reference assignment, SuccessfulRefits the number of usable refits, and InclusionProbability the proportion of those refits in which the participant returned to that cluster's label-matched subsample cluster. Model and Classes identify the candidate. Higher inclusion is better.

Stability$cluster_inclusion summarizes inclusion within each reference Cluster: MeanInclusion, P05Inclusion, and MinInclusion are the mean, fifth percentile, and minimum participant inclusion probabilities; Model and Classes identify the candidate. Higher values indicate that all, not only the average, of a cluster is recovered consistently.

Stability$coassignment is available only when the complete training cohort has at most coassignment_limit participants. Each candidate entry has a status of "available", "skipped", or "not_available"; reason explains a non-available result; matrix is the pairwise probability that two complete reference participants are assigned together across successful subsample refits; and row_ids maps matrix rows and columns to original training-row positions. Higher matrix values mean more consistent pairwise co-membership. Where more than one candidate is summarized, entries are named by its Model_Classes key. The matrix is diagnostic only and is never used for selection.

Stability$plots contains cluster_recovery (per-cluster Jaccard), partition_metrics (ARI, VI, NMI, and Fowlkes–Mallows distributions), and cluster_inclusion; it also contains a co-assignment heatmap when the matrix is available. These diagnostics complement rather than replace ARI and Jaccard: none can turn a poorly reproducible cluster into a stable phenotype.

Metric sources: Hubert and Arabie (1985) define ARI; Jaccard (1901) defines the overlap coefficient; Meila (2005) defines VI; Strehl and Ghosh (2002) describe NMI for partition comparison; Fowlkes and Mallows (1983) define their pairwise index; and Monti et al. (2003) describe resampling-based consensus co-assignment.

References

Saaty TL. The Analytic Hierarchy Process. McGraw-Hill, 1980. Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics. 1982;43:59-69. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5. J Stat Softw. 2016;71(11):1-29. Celeux G, Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. J Classification. 1996;13:195-212. Cavanaugh JE. A large-sample model selection criterion based on Kullback's symmetric divergence. Stat Methodol. 1999;61:165-180. Sclove SL. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika. 1987;52:333-343. Nylund KL, Asparouhov T, Muthen BO. Deciding on the number of classes in latent class analysis and growth mixture modeling. Struct Equ Modeling. 2007;14:535-569. Akogul S, Erisoglu M. An approach for determining the number of clusters in a model-based cluster analysis. Entropy. 2017;19:452.

Examples

# \donttest{
data("SimulatedPhenotypeData")
df_Training <- dplyr::filter(SimulatedPhenotypeData, .data$Cohort == "Training")
vars_Numeric <- paste0("Var", 1:12)

# Example-only display helper (not exported by SciDataReportR): round the
# numeric columns and add a frozen header.
ShowFitTable <- function(x, height = "320px") {
  x <- dplyr::mutate(
    dplyr::as_tibble(x),
    dplyr::across(dplyr::where(is.numeric), \(v) round(v, 3))
  )
  htmltools::browsable(htmltools::HTML(as.character(
    FreezeTableHeader(x, height = height, full_width = TRUE)
  )))
}

# Exploratory mode: every combination of k and model family. Model 2 is
# omitted here because it is the slowest to fit and, on this data, reaches
# the fit timeout at every k without contributing a candidate.
review <- CreateClusterModel_SOM_MClust(
  data = df_Training,
  variables = vars_Numeric,
  method = "exploratory",
  k_range = 2:5,
  models = c(1, 3),
  som_xdim = 5,
  som_ydim = 5,
  stability_resamples = 2,
  stability_seed = 20260805,
  Relabel = FALSE,
  min_nodes_per_cluster = NULL
)

# Every candidate solution
ShowFitTable(review$ModelInfo_MClust$fit_table)

 Model 
    Classes 
    LogLik 
    parameters 
    n 
    AIC 
    AWE 
    BIC 
    CAIC 
    CLC 
    KIC 
    SABIC 
    ICL 
    Entropy 
    prob_min 
    prob_max 
    BLRTStatistic 
    BLRTPValue 
    MinProfileNodeN 
    MaxProfileNodeN 
    MinProfileNodeProportion 
    MaxProfileNodeProportion 
    StabilitySuccessRate 
    StabilityARI_Mean 
    StabilityARI_P05 
    StabilityJaccard_Mean 
    StabilityJaccard_Min 
    ReproducibilityScore 
    AIC_scaled 
    BIC_scaled 
    Entropy_scaled 
    Reproducibility_scaled 
    ahp_index 
  
 1 
    2 
    -329.586 
    37 
    25 
    733.172 
    1006.502 
    778.270 
    815.270 
    661.038 
    773.172 
    663.530 
    -779.132 
    0.933 
    0.930 
    0.999 
    54.520 
    0.010 
    5 
    20 
    0.20 
    0.80 
    1 
    0.947 
    0.900 
    0.967 
    0.950 
    0.957 
    -1.291 
    -1.315 
    -2.217 
    0.809 
    -1.004 
  
 3 
    2 
    228.125 
    169 
    25 
    -118.249 
    1136.732 
    87.741 
    256.741 
    -454.251 
    53.751 
    -436.344 
    -87.744 
    0.999 
    1.000 
    1.000 
    23.218 
    0.376 
    4 
    21 
    0.16 
    0.84 
    1 
    0.947 
    0.900 
    0.967 
    0.950 
    0.957 
    0.885 
    0.951 
    0.500 
    0.809 
    0.786 
  
 1 
    3 
    -287.806 
    50 
    25 
    675.613 
    1045.563 
    736.556 
    786.556 
    577.549 
    728.613 
    581.502 
    -737.075 
    0.968 
    0.958 
    0.999 
    83.559 
    0.010 
    5 
    15 
    0.20 
    0.60 
    1 
    0.722 
    0.481 
    0.828 
    0.745 
    0.775 
    -1.144 
    -1.178 
    -0.769 
    -0.790 
    -0.970 
  
 3 
    3 
    240.315 
    182 
    25 
    -116.630 
    1235.046 
    105.205 
    287.205 
    -478.635 
    68.370 
    -459.194 
    -105.227 
    0.998 
    0.998 
    1.000 
    24.381 
    0.356 
    4 
    17 
    0.16 
    0.68 
    1 
    0.637 
    0.407 
    0.703 
    0.678 
    0.670 
    0.881 
    0.893 
    0.434 
    -1.709 
    0.125 
  
 1 
    4 
    -182.881 
    63 
    25 
    491.761 
    958.343 
    568.551 
    631.551 
    367.758 
    557.761 
    373.182 
    -568.566 
    0.999 
    0.999 
    1.000 
    209.851 
    0.010 
    5 
    8 
    0.20 
    0.32 
    1 
    0.983 
    0.976 
    0.988 
    0.975 
    0.985 
    -0.674 
    -0.627 
    0.470 
    1.058 
    0.057 
  
 3 
    4 
    258.702 
    195 
    25 
    -127.404 
    1320.957 
    110.276 
    305.276 
    -515.405 
    70.596 
    -494.437 
    -110.278 
    1.000 
    1.000 
    1.000 
    36.774 
    0.069 
    4 
    9 
    0.16 
    0.36 
    1 
    0.911 
    0.904 
    0.934 
    0.894 
    0.923 
    0.909 
    0.877 
    0.525 
    0.509 
    0.705 
  
 1 
    5 
    -140.289 
    76 
    25 
    432.579 
    995.848 
    525.213 
    601.213 
    282.579 
    511.579 
    289.530 
    -525.214 
    1.000 
    1.000 
    1.000 
    85.183 
    0.010 
    4 
    7 
    0.16 
    0.28 
    1 
    0.979 
    0.975 
    0.790 
    0.012 
    0.885 
    -0.523 
    -0.485 
    0.529 
    0.173 
    -0.076 
  
 3 
    5 
    280.916 
    208 
    25 
    -145.833 
    1399.220 
    107.694 
    315.694 
    -559.833 
    65.167 
    -537.334 
    -107.694 
    1.000 
    1.000 
    1.000 
    44.428 
    0.030 
    3 
    8 
    0.12 
    0.32 
    1 
    0.813 
    0.794 
    0.722 
    0.233 
    0.767 
    0.956 
    0.885 
    0.528 
    -0.858 
    0.377 
  

# Status, runtime, warnings, and errors for every attempted fit
ShowFitTable(
  review$ModelInfo_MClust$diagnostics$lpa_fit_diagnostics,
  height = "260px"
)

 Model 
    Classes 
    status 
    runtime_seconds 
    slow_fit 
    n_warnings 
    warnings 
    error 
  
 1 
    2 
    success 
    0.328 
    FALSE 
    0 
     
    NA 
  
 3 
    2 
    success 
    0.598 
    FALSE 
    0 
     
    NA 
  
 1 
    3 
    success 
    0.543 
    FALSE 
    0 
     
    NA 
  
 3 
    3 
    success 
    1.098 
    FALSE 
    0 
     
    NA 
  
 1 
    4 
    success 
    0.778 
    FALSE 
    0 
     
    NA 
  
 3 
    4 
    success 
    1.644 
    FALSE 
    0 
     
    NA 
  
 1 
    5 
    success 
    1.585 
    FALSE 
    0 
     
    NA 
  
 3 
    5 
    success 
    2.429 
    FALSE 
    0 
     
    NA 
  

review$ModelInfo_MClust$AHP$recommendation
#> [1] "AHP (AIC, BIC, Entropy, reproducibility) recommends Model 3 with k = 2 profiles."
ShowFitTable(review$Stability$summary, height = "260px")

 Model 
    Classes 
    StabilitySuccessRate 
    StabilityARI_Mean 
    StabilityARI_P05 
    StabilityJaccard_Mean 
    StabilityJaccard_Min 
    ReproducibilityScore 
  
 1 
    2 
    1 
    0.947 
    0.900 
    0.967 
    0.950 
    0.957 
  
 3 
    2 
    1 
    0.947 
    0.900 
    0.967 
    0.950 
    0.957 
  
 1 
    3 
    1 
    0.722 
    0.481 
    0.828 
    0.745 
    0.775 
  
 3 
    3 
    1 
    0.637 
    0.407 
    0.703 
    0.678 
    0.670 
  
 1 
    4 
    1 
    0.983 
    0.976 
    0.988 
    0.975 
    0.985 
  
 3 
    4 
    1 
    0.911 
    0.904 
    0.934 
    0.894 
    0.923 
  
 1 
    5 
    1 
    0.979 
    0.975 
    0.790 
    0.012 
    0.885 
  
 3 
    5 
    1 
    0.813 
    0.794 
    0.722 
    0.233 
    0.767 
  
review$fit_plot

review$fit_plot


# Refit the chosen solution to get the projectable model
model <- CreateClusterModel_SOM_MClust(
  data = df_Training, variables = vars_Numeric, method = "finalize",
  final_k = 4, final_model = 1, som_xdim = 5, som_ydim = 5,
  stability_resamples = 2, Relabel = FALSE,
  min_nodes_per_cluster = NULL
)
ShowFitTable(model$ModelInfo_MClust$fit_table, height = "160px")

 Model 
    Classes 
    LogLik 
    parameters 
    n 
    AIC 
    AWE 
    BIC 
    CAIC 
    CLC 
    KIC 
    SABIC 
    ICL 
    Entropy 
    prob_min 
    prob_max 
    BLRTStatistic 
    BLRTPValue 
    MinProfileNodeN 
    MaxProfileNodeN 
    MinProfileNodeProportion 
    MaxProfileNodeProportion 
    StabilitySuccessRate 
    StabilityARI_Mean 
    StabilityARI_P05 
    StabilityJaccard_Mean 
    StabilityJaccard_Min 
    ReproducibilityScore 
    AIC_scaled 
    BIC_scaled 
    Entropy_scaled 
    Reproducibility_scaled 
    ahp_index 
  
 1 
    4 
    -182.881 
    63 
    25 
    491.761 
    958.343 
    568.551 
    631.551 
    367.758 
    557.761 
    373.182 
    -568.566 
    0.999 
    0.999 
    1 
    209.851 
    0.01 
    5 
    8 
    0.2 
    0.32 
    1 
    0.983 
    0.976 
    0.988 
    0.975 
    0.985 
    0 
    0 
    0 
    0 
    0 
  
# }