Agglomerative Partitioning Framework for Dimension Reduction

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Documentation for package ‘partition’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

as_director	Create a custom director
as_measure	Create a custom metric
as_partitioner	Create a partitioner
as_partition_step	Create a partition object from a data frame
as_reducer	Create a custom reducer
baxter_clinical	Microbiome data
baxter_data	Microbiome data
baxter_data_dictionary	Microbiome data
baxter_family	Microbiome data
baxter_genus	Microbiome data
baxter_otu	Microbiome data
corr	Efficiently fit correlation coefficient for matrix or two vectors
direct_distance	Target based on minimum distance matrix
direct_distance_pearson	Target based on minimum distance matrix
direct_distance_spearman	Target based on minimum distance matrix
direct_k_cluster	Target based on K-means clustering
filter_reduced	Filter the reduced mappings
fitted.partition	Return the reduced data from a partition
icc	Calculate the intraclass correlation coefficient
is_partition	Is this object a partition?
is_partitioner	Is this object a partitioner?
is_partition_step	Is this object a 'partition_step'?
mapping_groups	Return partition mapping key
mapping_key	Return partition mapping key
map_cluster	Reduce a target
map_partition	Map a partition across a range of minimum information
measure_icc	Measure the information loss of reduction using intraclass correlation coefficient
measure_min_icc	Measure the information loss of reduction using the minimum intraclass correlation coefficient
measure_min_r2	Measure the information loss of reduction using minimum R-squared
measure_std_mutualinfo	Measure the information loss of reduction using standardized mutual information
measure_variance_explained	Measure the information loss of reduction using the variance explained
mutual_information	Calculate the standardized mutual information of a data set
partition	Agglomerative partitioning
partition_scores	Return the reduced data from a partition
part_icc	Partitioner: distance, ICC, scaled means
part_kmeans	Partitioner: K-means, ICC, scaled means
part_minr2	Partitioner: distance, minimum R-squared, scaled means
part_pc1	Partitioner: distance, first principal component, scaled means
part_stdmi	Partitioner: distance, mutual information, scaled means
permute_df	Permute a data set
plot_area_clusters	Plot partitions
plot_information	Plot partitions
plot_ncluster	Plot partitions
plot_permutation	Plot permutation tests
plot_stacked_area_clusters	Plot partitions
reduce_cluster	Reduce a target
reduce_first_component	Reduce selected variables to first principal component
reduce_kmeans	Reduce selected variables to scaled means
reduce_scaled_mean	Reduce selected variables to scaled means
replace_partitioner	Replace the director, metric, or reducer for a partitioner
scaled_mean	Average and scale rows in a 'data.frame'
simulate_block_data	Simulate correlated blocks of variables
super_partition	super_partition
test_permutation	Permute partitions
unnest_mappings	Return partition mapping key
unnest_reduced	Filter the reduced mappings