run_apriori_w_sample_names: Generate frequent items along with the names of supporting...

In RareComb: Combinatorial and Statistical Analyses of Rare Events

Generate frequent items along with the names of supporting observations using the apriori algorithm

Description

This function takes in a factorized Boolean matrix and generate frequent item sets that meet all the user provided criteria provided by the calling function. This function includes in it's output the identifiers of observations that support each significant combination.

Usage

run_apriori_w_sample_names(
  apriori_input_df,
  combo_length,
  support_threshold,
  input_colname_list,
  input_sample_list,
  confidence_threshold = confidence_threshold,
  include_output_ind = include_output_ind,
  output_colname_list = output_colname_list
)

Arguments

apriori_input_df	An input factorized Boolean dataframe with multiple input and outcome variables
combo_length	The length of the combinations specified by the user
support_threshold	Minimum support value calculated based on the minimum absolute observed frequency threshold specified by the user
input_colname_list	A list of column names that identify the input variables
input_sample_list	A list of row names that identify the samples/observations
confidence_threshold	Minimum confidence threshold specified by the user
include_output_ind	Specifies if the outcome variables must also be made part of the analysis using the algorithm
output_colname_list	A list of column names that identify the outcome variables

Details

This is a function leveraged by few of the four main methods available to the users.

Value

A list of frequent item sets that meet all the constraints supplied to the apriori algorithm

Author(s)

Vijay Kumar Pounraja

RareComb documentation built on March 18, 2022, 7:02 p.m.