This project contains the article and code for:
-
/manDocumentation files for functions -
/paperContains R code for figures, tables and supplementary material -
/RR Code for functions in the package -
/simScenarios considered in the article
Final version of article: <...>
First build and install the package in your environment.
Workflow for building the project: On a terminal, move to the folder and do
make sim_normal
make sim_survival
make figs
make tables
make pdf_with_diff
make supplementary
make submission
Please note that the simulations may take a long time to compute. To run just one
case, just use nohup R CMD BATCH --vanilla simulate.R in e.g. sim/normal/biom10
Session info:
> sessionInfo(package = NULL)
R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS
Matrix products: default
BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
LAPACK: /usr/lib/lapack/liblapack.so.3.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2 monomvn_1.9-7 lars_1.2
[4] pls_2.6-0 ggplot2_2.2.1 PITE_0.1.0
[7] R.utils_2.5.0 R.oo_1.22.0 R.methodsS3_1.7.1
[10] selectiveInference_1.2.2 survival_2.41-3 intervals_0.15.1
[13] knitr_1.20 MASS_7.3-50 glmnet_2.0-13
[16] foreach_1.4.3 Matrix_1.2-14 dplyr_0.7.4
[19] broom_0.4.2
loaded via a namespace (and not attached):
[1] Rcpp_0.12.11 pryr_0.1.2 highr_0.6 compiler_3.4.2
[5] plyr_1.8.4 bindr_0.1.1 iterators_1.0.9 tools_3.4.2
[9] digest_0.6.12 gtable_0.2.0 tibble_1.3.3 nlme_3.1-131
[13] lattice_0.20-35 pkgconfig_2.0.1 rlang_0.1.2 psych_1.6.6
[17] mvtnorm_1.0-7 stringr_1.2.0 rprojroot_1.3-2 grid_3.4.2
[21] glue_1.2.0 R6_2.2.2 tidyr_0.8.0 reshape2_1.4.2
[25] purrr_0.2.4 magrittr_1.5 backports_1.1.2 scales_0.4.1
[29] codetools_0.2-15 splines_3.4.2 assertthat_0.2.0 mnormt_1.5-5
[33] colorspace_1.3-2 labeling_0.3 quadprog_1.5-5 stringi_1.1.5
[37] lazyeval_0.2.1 munsell_0.4.3
The randomized lasso functions are implemented in the selectiveInference package
in python. We call python from inside R using the PythonInR R package.
To make it work, some steps need to be followed:
First install python developer files in a terminal.
sudo apt-get install python-dev
Install PythonInR package in R:
install.packages("PythonInR")
Install the python selective package from: https://github.com/selective-inference/Python-software I followed this on my terminal:
mkdir script
First install regreg package
git clone https://github.com/regreg/regreg
cd regreg
git checkout b8205eea21fdba690890768a1f181c6b29f0f194
sudo python setup.py install
cd ..
Now the selection package:
git clone https://github.com/selective-inference/Python-software
cd Python-software/
Make sure we are using the same version:
git checkout fc24a63fdc34cffc1f072ce7d6e94f82e5b3402f
Now clone the C code that the package uses:
git clone https://github.com/selective-inference/C-software/
cd C-software
git checkout 851279ffb326b145d00af45b87e7d857e3941ec9
cd ..
Now install the package
sudo python setup.py install
It may be needed to install additional packages in python. In my environment I had to install pip https://pip.pypa.io/en/stable/installing/#install-pip and then:
python -m pip install timeout-decorator --user
python -m pip install setuptools --user
python -m pip install cython --user
python -m pip install mpmath --user
python -m pip install pandas --user
python -m pip install nose --user
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