Comments (4)
Sure, I can upload the plotting scripts. They've bit rotted a bit so I have to fix them up. This whole thing was originally an IPython notebook and then I slowly pulled chunks out into separate files.
I just pushed some stuff reorganizing most of the code into a cliqs
folder with run_mindep.py
on the outside. Sorry not to go through the pull request process--I'm not quite a github pro yet so I wasn't sure how to make a pull request on my own repo.
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OK, I put in the analysis scripts.
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I see, I could almost run mindep_plots here: https://github.com/rht/cliqs/blob/jupyter/mindep_plots.ipynb (or https://nbviewer.jupyter.org/github/rht/cliqs/blob/jupyter/mindep_plots.ipynb). Each plot is chunked into separate cells.
stat_smooth(method="auto", mapping=aes(colour=real)) +
is commented out because otherwise the plots couldn't be rendered. I wonder if there is a dependency package that needs to be downloaded- There is an err near the end:
Error in
$<-.data.frame(
tmp, "p.less.than", value = "< .001"): replacement has 1 row, data has 0
(you have to activate the travis build at https://travis-ci.org/Futrell/cliqs)
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If I were to use one of the classifications of reproducibility in http://ropensci.github.io/reproducibility-guide/sections/introduction/:
- computational reproducibility[1]: this is still pending. There is also a question of whether the execution environment should be fully provided, preferably such that it ensures deterministic build, with a stronger guarantee than docker (via nixpkg!)[2]. Also, computational reproducibility should be easily automated, just like travis for code test cases.
- empirical reproducibility: the datasets in the repo http://tedlab.mit.edu/datasets/cliqs/ are well documented.
- statistical reproducibility: params are provided in the paper.
[1] https://gigascience.biomedcentral.com/articles/10.1186/s13742-016-0135-4
[2] http://www.sciencedirect.com/science/article/pii/S0167739X16000029
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