Package index • multiRF

Forest fitting

Fit directed multivariate random forests across omics blocks, discover optimal connections, and compute out-of-bag quantities.

fit_forest() fit_multi_forest() fit_rfsrc() fit_multi_rfsrc(): Fit random forest model and create model lists
fit_sub_mrf(): Fit an ensemble of sub-sampled MRF models for a single connection
fit_sub_multi_rfsrc(): Fit sub-MRF ensemble for a multi-omics connection list
find_connection(): Find optimal directional connections from fitted RF models
filter_omics(): Filter multi-omics feature blocks before RF fitting
compute_oob_forest_wt(): Compute OOB forest weight matrix
get_oob_nmse(): OOB normalized MSE for a fitted forest model

High-level entry points for the staged mrf3 pipeline, from initial fit to the full analysis.

Cluster samples from learned similarity, tune the number of clusters, and score agreement between clusterings.

mrf3_cl_prox(): MRF unsupervised clustering – Proximity method
mrf3_specific_clustering(): Joint Shared/Specific Similarity Clustering Wrapper
run_cluster_pipeline(): Run Shared/Specific Clustering Pipeline
tune_k_clusters() spectral_cl() pam_cl(): Tune the number of clusters
get_clusters(): Get cluster labels
cluster_similarity_matrix(): Cluster A Similarity Matrix
cluster_shared_similarity(): Cluster Shared Similarity From Reconstruction Weights
cluster_specific_similarity(): Cluster Specific Similarities Per Omics Block
cluster_metrics(): Compute multiple clustering metrics at once
cluster_ari() cluster_jaccard() cluster_nmi() cluster_purity(): Clustering Metrics
cluster_metric_matrix(): Pairwise metric matrix across multiple clusterings

Select informative features per omics block and tune top-v truncation parameters.

Forest weights, intersample measure of dissimilarity (IMD), reconstruction of shared and omics-specific structure, and related print methods.

get_weights(): Get IMD weights
get_multi_weights(): Get multi-omics weights
get_imp_forest(): Get forest importance
prepare_weight_matrix() adjust_weight_matrix() truncate_top_v_rows() row_normalize_weights(): Preprocess a weight matrix for downstream steps
build_similarity_from_weights(): Build Similarity Matrix From Forest Weights
mrf3_reconstr() get_reconstr_matrix(): MRF unsupervised clustering – Reconstruction method
get_shared_specific_weights(): Build Shared/Specific Weights From Reconstruction
get_shared_frac(): Compute Shared Fraction Per Omics Block
pairwise_imd(): Pairwise IMD Analysis After Model Fitting
cluster_imd(): Compute Cluster-Specific IMD Without Pairwise IMD
print(<pairwise_imd_analysis>): Print pairwise_imd_analysis
print(<cluster_imd>): Print cluster_imd

Stability evaluation of a fitted pipeline, summaries, and tidy accessors for downstream analysis.

Embeddings and plots for similarity matrices, clusters, weights, and survival outcomes.

plot(<mrf3_fit>): Plot an mrf3_fit object
mrf3_tsne(): compute tSNE embedding for MRF similarity matrix
plot_tsne() plot_network() plot_weights() plot_circos() plot_embed() plot_umap(): Multiple plot functions
plot_cluster_composition(): Plot cluster composition as a heatmap
plot_km(): Kaplan Meier plot

Bundled TCGA BRCA example data and multi-omics simulation.

tcga_brca: TCGA BRCA Expression Data
tcga_brca_clinical: TCGA BRCA Clinical Data
simulate_intersim_shared_specific(): Simulate Shared + Specific Multi-Omics Data via InterSIM

Accessors for components of fitted multiRF objects.

Backward-compatible aliases retained from earlier releases; use the replacements noted on each page.

simulate_shared_specific_intersim(): Backward-compatible alias of simulate_intersim_shared_specific()
fit_forest() fit_multi_forest() fit_rfsrc() fit_multi_rfsrc(): Fit random forest model and create model lists