summary: Summary

In emartchenko/mvsimstudy: Multivariate Correlated Data Simulation Tools

Description

Called after the analyze function within the simulation. Though analysis is finished this function still requires the method of analysis used in order to summarize the output correctly.

Usage

summary(data_to_summarize, e, analysis_method = NULL)

Arguments

data_to_summarize	Results of analyze function.
e	The effect_size object used in data generation. The 'answer key' to significant features found through analysis.
analysis_method	Analysis method used, see analyze documentation for more information. Default: NULL

Details

Best used from within the simulation functions to avoid any confusion with arguments.

Value

A list of 2. Includes the overall summary dataframe and the individual feature summary dataframe. Overall summary includes number of true positives, false positives, true negatives, false negatives, FDR, TPR/Sensitivity , and where applicable Model_p.value, AUC, and AUC_sd. Individual feature summary includes the term, proportion of times it was selected, and the user-specified effect size.

See Also

select

Examples

set.alpha(0.05)
e <- effect_size(c(0.3, 0, 0.7))
example <- generate_data(50, 50, c(1, 2, 3), c(0.3, 0, 0.7))
analyzed_example <- analyze(example, method= "ofaat")
summarized_example <- summary(analyzed_example, e, "ofaat")

emartchenko/mvsimstudy documentation built on May 24, 2019, 5:04 a.m.