Package index • neighborfinder

All functions

apply_NF_simple(): Apply NeighborFinder simplest version on raw data

apply_NeighborFinder(): Apply NeighborFinder on raw data

choose_params_values(): Render a table to give an indication of the values to choose for the prevalence level and the top filtering percentage

compute_precision(): Compute precision rate

compute_recall(): Compute recall rate

cvglm_to_coeffs_by_object(): Apply cv.glmnet() for a list of module IDs and for each prevalence level

data: data

final_step(): Gather lists of neighbors of true ones from the graph and detected ones from cv.glmnet()

find_all_module_neighbors(): Apply cv.glmnet() for a list of module IDs

find_module_neighbors(): Apply cv.glmnet() for a given mmodule ID

get_count_table(): Conversion to count table function with prevalence filter (Extracted from OneNet package)

graph_step(): Generate a graph with a "cluster-like" structure, only needed for simulation purposes

graphs: graphs

identify_module(): List the modules corresponding to a given object of interest

intersections_network(): Display the intersection network from 2 or more datasets

intersections_table(): Display the intersection table summarizing the results from 2 or more datasets

mclr(): Modified central log ratio (mclr) transformation extracted from the SPRING package

metadata: metadata

module_to_node(): Correspondence between the module ID (msp or functional module) and its name (bacteria or function)

new_synth_data(): Simulate data from some empirical count dataset with a "cluster-like" structure

norm_data(): Normalize data and filters it by prevalence level

prev_for_selected_nodes(): Extract edges in graph involving any module in object_of_interest set

res_by_filtering(): Give results from cvglm_to_coeffs_by_object() for each filtering top percentage

simulate_by_prevalence(): List the simulated count tables by level of prevalence

simulate_from_ecdf(): Simulate data (extracted from OneNet package) Generates synthetic count data based on empirical cumulative distribution (ecdf) of real count data

taxo: taxo

test_filter(): Render a table gathering precision and recall rates before and after filtering on coefficient values

truth_by_prevalence(): Give true neighbors by level of prevalence

visualize_network(): Display network after applying NeighborFinder