SteinerNet (version 2.0)

steiner_simulation: steiner_simulation

Description

This function executes Steiner algorithms on simulated data and stores their results into files.

Usage

steiner_simulation(test,listofterminaltest,repetition,testfolder = NULL)

Arguments

test

test selects the test type to apply the simulation. It can be exact, appr, or enum.

listofterminaltest

listofterminaltest is an input list. Elements of the list are number of terminals that are selected for a simulation.

repetition

repetition is a list of probabilities. Its length declares the number of elements if random data set that is created for each terminal number.

testfolder

testfolder specifies a folder name to read the simulated data from it and to store Steiner tree algorithms results inside it.

Value

The function stores the result of execution of Steiner trees and the time of their executions in an address that is stated in testfolder.

Details

This function performs three type of experiments. Test specifies the type of comparison to perform.

test can be exact or appr or enum,

exact refers to executing the set of steiner tree algorithms including the exact algorithm.

appr forces to executing the set of steiner tree algorithms without the exact algorithm.

enum refers to to executing the set of steiner tree enumeration algorithms.

listofterminaltest in our study was made of 5, 8, 20, 50, 70 for comparing approximate algorithms and it was 5, 8 for experiments that included exact Steiner tree algorithm. [1]

If testfolder is NULL, it will use default value "steinerdata2" for folder when test is exact and "steinerdataenum" when test is enum.

In our study, we repeated the tests 50 times, and we made the random walk to select a node to be terminal with 0.5 probability. Therefore repetition in our comparison was a list of fifty 0.5 values. We also survied the behavior of the algorithms when selection probability was and 0.2 and 0.8.

References

1. Afshin Sadeghi and Holger Froehlich, "Steiner tree methods for optimal sub-network identification: an empirical study", BMC Bioinformatics 2013 14:144.

Examples

# NOT RUN {
       library(igraph)
       library(SteinerNet)
       g <- igraph::graph.ring(10)
       
# }
# NOT RUN {
       
# }
# NOT RUN {
                generate_st_samples("exact", g, "testfolder", c(2,3), c(.8,.8))
                steiner_simulation("exact", "testfolder", c(2,3), c(.8,.8))
       
# }