R: Agglomerative Partitioning Framework for Dimension Reduction

# Agglomerative Partitioning Framework for Dimension Reduction

## Documentation for package ‘partition’ version 0.2.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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