Package: partition 0.2.1.9000

Malcolm Barrett

partition: Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

Authors:Joshua Millstein [aut], Malcolm Barrett [aut, cre], Katelyn Queen [aut]

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partition.pdf |partition.html✨
partition/json (API)
NEWS

# Install 'partition' in R:
install.packages('partition', repos = c('https://uscbiostats.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/uscbiostats/partition/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

data-reductiondimensionality-reductionpartitional-clustering

50 exports 34 stars 3.04 score 44 dependencies 1 dependents 2 mentions 27 scripts 380 downloads

Last updated 4 months agofrom:5f7366a9e7. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-win-x86_64OKAug 20 2024
R-4.5-linux-x86_64OKAug 20 2024
R-4.4-win-x86_64OKAug 20 2024
R-4.4-mac-x86_64OKAug 20 2024
R-4.4-mac-aarch64OKAug 20 2024
R-4.3-win-x86_64OKAug 20 2024
R-4.3-mac-x86_64OKAug 20 2024
R-4.3-mac-aarch64OKAug 20 2024

Exports:%>%as_directoras_measureas_partition_stepas_partitioneras_reducercorrdirect_distancedirect_distance_pearsondirect_distance_spearmandirect_k_clusterfilter_reducediccis_partitionis_partition_stepis_partitionermap_clustermap_partitionmapping_groupsmapping_keymeasure_iccmeasure_min_iccmeasure_min_r2measure_std_mutualinfomeasure_variance_explainedmutual_informationpart_iccpart_kmeanspart_minr2part_pc1part_stdmipartitionpartition_scorespermute_dfplot_area_clustersplot_informationplot_nclusterplot_permutationplot_stacked_area_clustersreduce_clusterreduce_first_componentreduce_kmeansreduce_scaled_meanreplace_partitionerscaled_meansimulate_block_datasuper_partitiontest_permutationunnest_mappingsunnest_reduced

Dependencies:clicolorspacecpp11crayondplyrfansifarverforcatsgenericsggplot2gluegtablehmsinfotheoisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadillorlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Extending partition

Malcolm Barrett

Rendered fromextending-partition.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2024-05-22
Started: 2019-05-13

Introduction to Partition

Malcolm Barrett

Rendered fromintroduction-to-partition.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2024-05-22
Started: 2019-05-13

Readme and manuals

Help Manual

Help pageTopics
Create a custom directoras_director
Create a custom metricas_measure
Create a partition object from a data frameas_partition_step
Create a partitioneras_partitioner
Create a custom reduceras_reducer
Microbiome databaxter_clinical baxter_data baxter_data_dictionary baxter_family baxter_genus baxter_otu
Efficiently fit correlation coefficient for matrix or two vectorscorr
Target based on minimum distance matrixdirect_distance direct_distance_pearson direct_distance_spearman
Target based on K-means clusteringdirect_k_cluster
Filter the reduced mappingsfilter_reduced unnest_reduced
Calculate the intraclass correlation coefficienticc
Is this object a partition?is_partition
Is this object a 'partition_step'?is_partition_step
Is this object a partitioner?is_partitioner
Map a partition across a range of minimum informationmap_partition
Return partition mapping keymapping_groups mapping_key unnest_mappings
Measure the information loss of reduction using intraclass correlation coefficientmeasure_icc
Measure the information loss of reduction using the minimum intraclass correlation coefficientmeasure_min_icc
Measure the information loss of reduction using minimum R-squaredmeasure_min_r2
Measure the information loss of reduction using standardized mutual informationmeasure_std_mutualinfo
Measure the information loss of reduction using the variance explainedmeasure_variance_explained
Calculate the standardized mutual information of a data setmutual_information
Partitioner: distance, ICC, scaled meanspart_icc
Partitioner: K-means, ICC, scaled meanspart_kmeans
Partitioner: distance, minimum R-squared, scaled meanspart_minr2
Partitioner: distance, first principal component, scaled meanspart_pc1
Partitioner: distance, mutual information, scaled meanspart_stdmi
Agglomerative partitioningpartition
Return the reduced data from a partitionfitted.partition partition_scores
Permute a data setpermute_df
Plot partitionsplot_area_clusters plot_information plot_ncluster plot_stacked_area_clusters
Plot permutation testsplot_permutation
Reduce a targetmap_cluster reduce_cluster
Reduce selected variables to first principal componentreduce_first_component
Reduce selected variables to scaled meansreduce_kmeans
Reduce selected variables to scaled meansreduce_scaled_mean
Replace the director, metric, or reducer for a partitionerreplace_partitioner
Average and scale rows in a 'data.frame'scaled_mean
Simulate correlated blocks of variablessimulate_block_data
super_partitionsuper_partition
Permute partitionstest_permutation