Last updated: 2019-01-17
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed:
set.seed(20190115)
The command set.seed(20190115) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded
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✔ Repository version: 2b216ae
wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: .sos/
Ignored: data/.DS_Store
Untracked files:
Untracked: .gitattributes
Untracked: analysis/SusieZConverge.Rmd
Untracked: data/sim_gaussian_475.rds
Untracked: data/sim_gaussian_75.rds
Untracked: output/dscout_gaussian_init.rds
Untracked: output/dscout_gaussian_null.rds
Untracked: output/dscout_gaussian_z.rds
Unstaged changes:
Modified: README.md
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
library(dscrutils)
dscout_null = dscquery(dsc.outdir='output/susie_z_gaussian_benchmark', targets='sim_gaussian_null susie_z score susie_z.avg_purity susie_z.niter score.hit score.signal_num score.cs_medianSize score.top_hit')
saveRDS(dscout_null, 'output/susie_z_gaussian_benchmark/dscout_gaussian_null.rds')
dscout_gaussian_z = dscquery(dsc.outdir='output/susie_z_gaussian_benchmark', targets='sim_gaussian susie_z score sim_gaussian.effect_num sim_gaussian.pve sim_gaussian.sigma sim_gaussian.mean_corX susie_z.avg_purity susie_z.niter susie_z.L score.hit score.signal_num score.cs_medianSize score.top_hit')
saveRDS(dscout_gaussian_z, 'output/susie_z_gaussian_benchmark/dscout_gaussian_z.rds')
dscout_gaussian_init = dscquery(dsc.outdir='output/susie_z_gaussian_benchmark', targets='sim_gaussian susie_z_init score sim_gaussian.effect_num sim_gaussian.pve sim_gaussian.sigma sim_gaussian.mean_corX susie_z_init.avg_purity susie_z_init.niter score.hit score.signal_num score.cs_medianSize score.top_hit')
saveRDS(dscout_gaussian_init, 'output/susie_z_gaussian_benchmark/dscout_gaussian_init.rds')
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.1.1 Rcpp_1.0.0 digest_0.6.18
[4] rprojroot_1.3-2 R.methodsS3_1.7.1 backports_1.1.3
[7] git2r_0.24.0 magrittr_1.5 evaluate_0.12
[10] stringi_1.2.4 whisker_0.3-2 R.oo_1.22.0
[13] R.utils_2.7.0 rmarkdown_1.11 tools_3.5.1
[16] stringr_1.3.1 yaml_2.2.0 compiler_3.5.1
[19] htmltools_0.3.6 knitr_1.20
This reproducible R Markdown analysis was created with workflowr 1.1.1