Package index • seriation

Seriation Methods

Create a seriation order which is a permutation of objects.

seriate(): Seriate Dissimilarity Matrices, Matrices or Arrays
seriate_best() seriate_rep() seriate_improve(): Best Seriation
register_DendSer(): Register Seriation Methods from Package DendSer
register_GA() gaperm_mixedMutation(): Register a Genetic Algorithm Seriation Method
register_optics(): Register Seriation Based on OPTICS
register_smacof(): Register Seriation Methods from Package smacof
register_tsne(): Register Seriation Based on 1D t-SNE
register_umap(): Register Seriation Based on 1D UMAP
register_vegan(): Register Seriation Methods from Package vegan
registry_seriate list_seriation_methods() get_seriation_method() set_seriation_method() print(<seriation_method>): Registry for Seriation Methods

Calculate seriation creteria for a seriation order.

criterion(): Criterion for a Loss/Merit Function for Data Given a Permutation
registry_criterion list_criterion_methods() get_criterion_method() set_criterion_method() print(<criterion_method>): Registry for Criterion Methods

Work with permutations returned by seriation methods.

get_order() get_rank() get_permutation_matrix(): Extracting Order Information from a Permutation Object
permute(): Permute the Order in Various Objects
permutation_vector2matrix() permutation_matrix2vector(): Conversion Between Permutation Vector and Permutation Matrix
reorder(<hclust>): Reorder Dendrograms using Optimal Leaf Ordering
ser_dist() ser_cor() ser_align(): Dissimilarities and Correlations Between Seriation Orders
ser_permutation() print(<ser_permutation>) summary(<ser_permutation>) c(<ser_permutation>) `[`(<ser_permutation>): Class ser_permutation – A Collection of Permutation Vectors for Seriation
ser_permutation_vector() c(<ser_permutation_vector>) rev(<ser_permutation_vector>) get_method() length(<ser_permutation_vector>) print(<ser_permutation_vector>) summary(<ser_permutation_vector>): Class ser_permutation_vector – A Single Permutation Vector for Seriation

Visualize seriated data to discover patterns.

VAT() iVAT() path_dist() ggVAT() ggiVAT(): Visual Analysis for Cluster Tendency Assessment (VAT/iVAT)
bertinplot() panel.bars() panel.circles() panel.rectangles() panel.squares() panel.tiles() panel.blocks() panel.lines() bertin_cut_line() ggbertinplot(): Plot a Bertin Matrix
dissplot() plot(<reordered_cluster_dissimilarity_matrix>) print(<reordered_cluster_dissimilarity_matrix>) ggdissplot(): Dissimilarity Plot
hmap() gghmap(): Plot Heat Map Reordered Using Seriation
bluered() greenred() reds() blues() greens() greys() grays(): Different Useful Color Palettes
pimage() ggpimage(): Permutation Image Plot

Chameleon chameleon chameleon_ds4 chameleon_ds5 chameleon_ds7 chameleon_ds8: 2D Data Sets used for the CHAMELEON Clustering Algorithm
Irish: Irish Referendum Data Set
Munsingen: Hodson's Munsingen Data Set
Psych24: Results of 24 Psychological Test for 8th Grade Students
SupremeCourt: Voting Patterns in the Second Rehnquist U.S. Supreme Court
Townships: Bertin's Characteristics of Townships
Wood: Gene Expression Data for Wood Formation in Poplar Trees
Zoo: Zoo Data Set
create_lines_data() create_ordered_data(): Create Simulated Data for Seriation Evaluation
is.robinson() random.robinson(): Create and Recognize Robinson and Pre-Robinson Matrices

LS_swap() LS_insert() LS_reverse() LS_mixed(): Neighborhood functions for Seriation Method SA
lle(): Locally Linear Embedding (LLE)
uniscale() MDS_stress() get_config() plot_config(): Fit an Unidimensional Scaling for a Seriation Order