RareComb (version 1.1)

analyze_in_out_simultaneity: Analyze relationships between rare events among multiple input and output variables

Description

This function takes a Boolean dataframe as input and analyzes the relationship between input and output variables for the combinations that that include at least a single output variable andmeet all the input criteria specified by the user.

Usage

analyze_in_out_simultaneity(boolean_input_mult_df, combo_length, min_output_count,
                            max_output_count, min_indv_threshold, max_freq_threshold,
                            input_format, output_format, pval_filter_threshold,
                            adj_pval_type)

Arguments

boolean_input_mult_df

An input Boolean dataframe with multiple input and outcome variables

combo_length

The length of the combinations specified by the user

min_output_count

Minimum number of output variables present in the combination

max_output_count

Maximum number of output variables present in the combination

min_indv_threshold

Minimum number of instances that support the combination

max_freq_threshold

Maximum fraction of the cohort size that could support a combination (i.e., filter out highly frequent events)

input_format

Optional | Naming convention used for input variables (Default = 'Input_')

output_format

Optional | Naming convention used for output variables (Default = 'Output_')

pval_filter_threshold

Optional | p-value cut-off to use to identify significant combinations (Default = 0.05)

adj_pval_type

Optional | Type of multiple testing corrections to use (Default = 'BH'; Alternative option = 'bonferroni')

Value

A dataframe with the list of multiple-testing adjusted statistically significant combinations along with quantitative measures (frequencies, p-values etc) that support the findings.

Examples

# NOT RUN {
    analyze_in_out_simultaneity(boolean_input_mult_df, 3, 1, 2, 5, 0.25,
                                input_format = 'Input_', output_format = 'Output_',
                                pval_filter_threshold = 0.05, adj_pval_type = 'BH')


# }