Last updated: 2020-04-18

Checks: 7 0

Knit directory: 2019-feature-selection/

This reproducible R Markdown analysis was created with workflowr (version 1.6.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

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.

The command set.seed(20190522) 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.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version b4fda43. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use 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:    .Ruserdata/
    Ignored:    .drake/
    Ignored:    .vscode/
    Ignored:    analysis/rosm.cache/
    Ignored:    data/
    Ignored:    inst/Benchmark for Filter Methods for Feature Selection in High-Dimensional  Classification Data.pdf
    Ignored:    inst/study-area-map/._study-area.qgs
    Ignored:    inst/study-area-map/study-area.qgs~
    Ignored:    log/
    Ignored:    renv/library/
    Ignored:    renv/staging/
    Ignored:    reviews/
    Ignored:    rosm.cache/

Untracked files:
    Untracked:  temporary-bmr.rda

Unstaged changes:
    Modified:   R/04-fun-sentinel-processing.R
    Modified:   _drake.R
    Modified:   analysis/report-defoliation.Rmd
    Modified:   code/03-sentinel-processing.R
    Modified:   code/071-benchmark-matrix-buffer2.R
    Modified:   code/081-aggregate_buffer2.R

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.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/eval-performance.Rmd) and HTML (docs/eval-performance.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd b4fda43 pat-s 2020-04-18 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 6c42b7c pat-s 2020-04-18 Build site.
Rmd 8c30483 pat-s 2020-04-18 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 544e288 pat-s 2020-04-12 Build site.
Rmd ce72956 pat-s 2020-04-12 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 6bded83 pat-s 2020-03-05 Build site.
Rmd 21453cb pat-s 2020-03-05 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html f59d02a pat-s 2020-03-05 Build site.
Rmd 167fdbc pat-s 2020-03-05 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 2ee982d pat-s 2020-03-05 Build site.
Rmd d487d51 pat-s 2020-03-05 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 776b35f pat-s 2020-03-03 Build site.
Rmd d0c645a pat-s 2020-03-03 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 274a918 pat-s 2020-02-25 Build site.
Rmd 2e632e1 pat-s 2020-02-25 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
Rmd a53ea68 pat-s 2020-02-24 add prefixes to plots and tables
Rmd 379bd5d pat-s 2020-02-24 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html f680221 pat-s 2020-01-15 Build site.
html b25e779 pat-s 2020-01-10 Build site.
Rmd db0baaa pat-s 2020-01-10 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 7f9507f pat-s 2019-12-10 Build site.
Rmd 951e98c pat-s 2019-12-10 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE, verbose =
Rmd b27c623 pat-s 2019-12-09 Standard Error -> SE
Rmd 559a59d pat-s 2019-12-09 add scatterplots to vis BM perf
html 482a158 pat-s 2019-11-01 Build site.
html becf5ea pat-s 2019-11-01 Build site.
html bd7c7f5 pat-s 2019-10-31 Build site.
html 62ff96f pat-s 2019-10-07 Build site.
html a947654 pat-s 2019-10-02 Build site.
html 49da171 pat-s 2019-09-22 Build site.
html c6317a8 pat-s 2019-09-19 Build site.
Rmd d7c72a8 pat-s 2019-09-19 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 7fd40ca pat-s 2019-09-18 Build site.
Rmd 44ff84b pat-s 2019-09-18 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html 41aae14 pat-s 2019-09-12 Build site.
html ff340b8 pat-s 2019-09-03 Build site.
Rmd a524819 pat-s 2019-09-03 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
html b181c52 pat-s 2019-09-02 Build site.
Rmd cf6e820 pat-s 2019-09-02 wflow_publish(“analysis/eval-performance.Rmd”)
Rmd 1bec10d pat-s 2019-09-01 no timestamps in latex tables
html 4e363ac pat-s 2019-09-01 Build site.
Rmd 518d0cb pat-s 2019-09-01 style files using tidyverse style
html 8e7e4fe pat-s 2019-09-01 Build site.
Rmd 8941bca pat-s 2019-09-01 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
Rmd 297ed93 pat-s 2019-08-31 add filter vs no filter comparison plot
html 7582c67 pat-s 2019-08-31 Build site.
html abd531f pat-s 2019-08-31 Build site.
Rmd 9117eee pat-s 2019-08-31 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
Rmd 5b629cb pat-s 2019-08-19 add new tasks to performance eval report
html 1ec8768 pat-s 2019-08-17 Build site.
html df85aba pat-s 2019-07-12 Build site.
html 3a44a95 pat-s 2019-07-10 Build site.
html c238ce4 pat-s 2019-07-10 Build site.
Rmd e98cb01 pat-s 2019-07-10 wflow_publish(knitr_in(“analysis/eval-performance.Rmd”), view = FALSE,
Rmd 24e318f pat-s 2019-07-01 update reports
Rmd ca5c5bc pat-s 2019-06-28 add eval-performance report

Last update:

[1] "Sat Apr 18 16:33:13 2020"

Fold performances of “SVM MBO No Filter” on the HR Task

Plot

RMSE

Luiando

21.17

Laukiz1

28.05

Laukiz2

9.00

Oiartzun

54.26

(Table) T1 All leaner/filter/task combinations ordered by performance.

Overall leaderboard across all settings, sorted ascending by performance.

Task

Model

Filter

RMSE

SE

NRI

SVM

Info Gain

2.799100e+01

1.915200e+01

HR-NRI-VI

SVM

Relief

2.806700e+01

1.914300e+01

VI

SVM

Relief

2.809700e+01

1.913500e+01

HR-NRI-VI

SVM

Car

2.810700e+01

1.912600e+01

HR-NRI

SVM

MRMR

2.811700e+01

1.911300e+01

VI

SVM

Pearson

2.811900e+01

1.909900e+01

HR-NRI

SVM

CMIM

2.811900e+01

1.909000e+01

HR

SVM

Info Gain

2.812200e+01

1.912300e+01

HR

SVM

CMIM

2.812200e+01

1.912300e+01

NRI-VI

SVM

PCA

2.812200e+01

1.912300e+01

HR-NRI

SVM

PCA

2.812200e+01

1.912300e+01

HR-NRI-VI

SVM

PCA

2.812200e+01

1.912300e+01

NRI-VI

SVM

No Filter

2.812200e+01

1.912300e+01

HR-NRI

SVM

No Filter

2.812200e+01

1.912300e+01

HR

SVM

Car

2.812300e+01

1.912300e+01

HR

SVM

Borda

2.812300e+01

1.912300e+01

NRI

SVM

PCA

2.812300e+01

1.912300e+01

HR

SVM

No Filter

2.812300e+01

1.912300e+01

VI

SVM

No Filter

2.812300e+01

1.912300e+01

NRI

SVM

No Filter

2.812300e+01

1.912300e+01

HR-NRI-VI

SVM

No Filter

2.812300e+01

1.912300e+01

HR

SVM

Pearson

2.812400e+01

1.912300e+01

NRI-VI

SVM

Car

2.812500e+01

1.912900e+01

VI

SVM

MRMR

2.812500e+01

1.911900e+01

HR

SVM

PCA

2.812500e+01

1.912200e+01

VI

SVM

Info Gain

2.812600e+01

1.912300e+01

HR-NRI-VI

SVM

MRMR

2.812600e+01

1.910000e+01

HR

SVM

Relief

2.813000e+01

1.911400e+01

HR-NRI-VI

SVM

Borda

2.813300e+01

1.936200e+01

VI

SVM

PCA

2.814400e+01

1.909100e+01

HR

SVM

MRMR

2.815300e+01

1.912300e+01

VI

SVM

Borda

2.815500e+01

1.918300e+01

NRI-VI

SVM

CMIM

2.815700e+01

1.907700e+01

HR-NRI-VI

SVM

CMIM

2.816200e+01

1.907100e+01

VI

SVM

CMIM

2.816900e+01

1.921800e+01

HR-NRI

SVM

Info Gain

2.817500e+01

1.913100e+01

NRI-VI

SVM

Borda

2.818100e+01

1.904500e+01

HR-NRI-VI

SVM

Info Gain

2.820400e+01

1.902300e+01

HR-NRI

SVM

Borda

2.820500e+01

1.900700e+01

HR-NRI

SVM

Pearson

2.821300e+01

1.915100e+01

VI

SVM

Car

2.826300e+01

1.927700e+01

NRI

SVM

Pearson

2.832100e+01

1.894200e+01

HR-NRI

SVM

Relief

2.833700e+01

1.884000e+01

HR-NRI

SVM

Car

2.838400e+01

1.876200e+01

NRI

SVM

Car

2.840300e+01

1.874400e+01

NRI-VI

SVM

MRMR

2.844000e+01

1.875300e+01

HR-NRI-VI

SVM

Pearson

2.846900e+01

1.864000e+01

NRI

SVM

MRMR

2.851700e+01

1.860700e+01

NRI-VI

SVM

Relief

2.857500e+01

1.871000e+01

NRI

SVM

CMIM

2.865700e+01

1.842800e+01

NRI-VI

SVM

Info Gain

2.866400e+01

1.829900e+01

NRI

SVM

Borda

2.867900e+01

1.840000e+01

NRI-VI

SVM

Pearson

2.873800e+01

1.853400e+01

NRI

RF

Car

3.077400e+01

1.686100e+01

VI

Lasso-MBO

No Filter

3.100900e+01

1.471400e+01

HR-NRI-VI

XGBOOST

Borda

3.105200e+01

1.700900e+01

HR-NRI

XGBOOST

CMIM

3.108900e+01

1.657600e+01

NRI

Lasso-MBO

No Filter

3.116500e+01

1.502500e+01

NRI

Ridge-MBO

No Filter

3.116500e+01

1.502500e+01

NRI-VI

Lasso-MBO

No Filter

3.120100e+01

1.508900e+01

HR-NRI

Lasso-MBO

No Filter

3.120100e+01

1.508900e+01

HR-NRI-VI

Lasso-MBO

No Filter

3.120100e+01

1.508900e+01

NRI-VI

XGBOOST

Car

3.208200e+01

1.767200e+01

NRI-VI

XGBOOST

MRMR

3.259500e+01

1.627900e+01

NRI-VI

XGBOOST

CMIM

3.272100e+01

1.543600e+01

HR-NRI

XGBOOST

Relief

3.274300e+01

1.409400e+01

NRI

RF

PCA

3.296600e+01

1.616100e+01

NRI

RF

Borda

3.305000e+01

1.485800e+01

NRI-VI

RF

PCA

3.309600e+01

1.600100e+01

NRI

XGBOOST

Car

3.315400e+01

1.640300e+01

HR-NRI

RF

PCA

3.325500e+01

1.572300e+01

NRI-VI

XGBOOST

No Filter

3.350500e+01

1.459900e+01

HR-NRI-VI

RF

PCA

3.358800e+01

1.571000e+01

HR-NRI-VI

RF

Car

3.377900e+01

1.429700e+01

HR-NRI

RF

Car

3.404400e+01

1.343400e+01

NRI

XGBOOST

CMIM

3.416800e+01

1.557100e+01

HR-NRI-VI

XGBOOST

MRMR

3.417000e+01

1.494000e+01

NRI

SVM

Relief

3.417700e+01

1.990500e+01

NRI

XGBOOST

Pearson

3.420900e+01

1.224600e+01

HR-NRI

XGBOOST

Pearson

3.423400e+01

1.373400e+01

NRI

XGBOOST

MRMR

3.439300e+01

1.403700e+01

HR

RF

Car

3.446700e+01

1.260400e+01

HR-NRI-VI

XGBOOST

Pearson

3.451600e+01

1.425300e+01

NRI-VI

XGBOOST

Info Gain

3.451800e+01

1.500500e+01

HR-NRI

XGBOOST

Borda

3.459000e+01

1.488700e+01

NRI-VI

XGBOOST

Relief

3.470900e+01

1.451300e+01

NRI

RF

CMIM

3.473400e+01

1.373300e+01

HR

XGBOOST

Borda

3.474800e+01

1.357200e+01

HR-NRI-VI

RF

CMIM

3.476500e+01

1.382300e+01

NRI-VI

XGBOOST

Pearson

3.477100e+01

1.353400e+01

HR-NRI-VI

XGBOOST

No Filter

3.477400e+01

1.380400e+01

HR

XGBOOST

Relief

3.477500e+01

1.331700e+01

HR-NRI-VI

XGBOOST

CMIM

3.479700e+01

1.475800e+01

HR-NRI-VI

XGBOOST

Car

3.480000e+01

1.470200e+01

HR

XGBOOST

Car

3.480600e+01

1.291500e+01

NRI

RF

MRMR

3.484300e+01

1.393700e+01

HR-NRI

RF

MRMR

3.489500e+01

1.378100e+01

HR-NRI-VI

XGBOOST

Info Gain

3.490000e+01

1.361800e+01

NRI

XGBOOST

Info Gain

3.490400e+01

1.579900e+01

HR-NRI

XGBOOST

No Filter

3.496300e+01

1.376900e+01

NRI-VI

RF

Car

3.499600e+01

1.330800e+01

HR-NRI-VI

RF

MRMR

3.500700e+01

1.363400e+01

NRI-VI

RF

CMIM

3.518900e+01

1.417200e+01

NRI-VI

RF

MRMR

3.522300e+01

1.412900e+01

HR-NRI-VI

XGBOOST

Relief

3.524600e+01

1.282000e+01

HR

Lasso-MBO

No Filter

3.526100e+01

1.224300e+01

HR

XGBOOST

CMIM

3.527600e+01

1.366500e+01

HR

XGBOOST

Pearson

3.532500e+01

1.367800e+01

HR

Ridge-MBO

No Filter

3.545300e+01

1.265500e+01

HR

RF

Borda

3.559300e+01

1.294900e+01

NRI-VI

XGBOOST

Borda

3.560800e+01

1.182300e+01

NRI

XGBOOST

Borda

3.564000e+01

1.236400e+01

NRI-VI

RF

Borda

3.569100e+01

1.405100e+01

HR

RF

Info Gain

3.582500e+01

1.384800e+01

HR-NRI

XGBOOST

Info Gain

3.584200e+01

1.454300e+01

NRI

RF

No Filter

3.586800e+01

1.287300e+01

HR

XGBOOST

MRMR

3.586900e+01

1.386600e+01

NRI-VI

RF

No Filter

3.599900e+01

1.289500e+01

NRI

XGBOOST

No Filter

3.605100e+01

1.160400e+01

NRI

XGBOOST

Relief

3.606200e+01

1.277900e+01

HR-NRI

RF

Borda

3.607400e+01

1.378700e+01

HR-NRI

RF

No Filter

3.622200e+01

1.283300e+01

HR-NRI

RF

CMIM

3.631700e+01

1.414400e+01

HR-NRI-VI

RF

No Filter

3.635600e+01

1.263800e+01

HR-NRI

RF

Info Gain

3.643200e+01

1.394300e+01

NRI

RF

Info Gain

3.645200e+01

1.382700e+01

HR-NRI-VI

RF

Info Gain

3.656000e+01

1.371300e+01

NRI-VI

RF

Info Gain

3.657500e+01

1.385600e+01

HR-NRI-VI

RF

Borda

3.665400e+01

1.305000e+01

HR-NRI

RF

Pearson

3.688300e+01

1.361900e+01

NRI-VI

RF

Pearson

3.690300e+01

1.349500e+01

HR

RF

Pearson

3.692000e+01

1.445700e+01

HR-NRI

XGBOOST

MRMR

3.701000e+01

1.314700e+01

NRI

RF

Pearson

3.714100e+01

1.326600e+01

VI

XGBOOST

Relief

3.714700e+01

1.426200e+01

HR

RF

PCA

3.715300e+01

1.232900e+01

HR-NRI-VI

RF

Pearson

3.730000e+01

1.344300e+01

NRI

RF

Relief

3.737600e+01

9.425000e+00

HR-NRI

XGBOOST

Car

3.757200e+01

1.110100e+01

HR-NRI

RF

Relief

3.769000e+01

9.839000e+00

VI

RF

MRMR

3.818100e+01

1.225500e+01

NRI

XGBOOST

PCA

3.842900e+01

1.498200e+01

NRI-VI

RF

Relief

3.849800e+01

8.671000e+00

HR-NRI-VI

RF

Relief

3.849800e+01

8.909000e+00

VI

RF

No Filter

3.875400e+01

1.127100e+01

VI

RF

Borda

3.884000e+01

1.123700e+01

HR

RF

CMIM

3.968700e+01

8.106000e+00

VI

XGBOOST

Info Gain

3.987200e+01

1.088000e+01

VI

RF

Info Gain

3.992000e+01

9.458000e+00

HR

XGBOOST

Info Gain

4.007000e+01

1.074300e+01

VI

RF

CMIM

4.009900e+01

9.758000e+00

VI

RF

Pearson

4.017500e+01

9.959000e+00

HR-NRI-VI

XGBOOST

PCA

4.018500e+01

1.139100e+01

VI

RF

Relief

4.044900e+01

8.367000e+00

NRI-VI

XGBOOST

PCA

4.056700e+01

1.083900e+01

VI

RF

Car

4.058100e+01

1.244800e+01

VI

RF

PCA

4.071900e+01

8.567000e+00

HR

RF

Relief

4.165600e+01

1.064000e+01

VI

XGBOOST

CMIM

4.172700e+01

9.810000e+00

HR

RF

No Filter

4.188400e+01

7.806000e+00

HR

XGBOOST

PCA

4.214800e+01

8.077000e+00

HR

RF

MRMR

4.231200e+01

7.705000e+00

HR-NRI

XGBOOST

PCA

4.257800e+01

8.278000e+00

VI

XGBOOST

PCA

4.366700e+01

8.067000e+00

VI

XGBOOST

Pearson

4.461000e+01

5.783000e+00

VI

XGBOOST

No Filter

4.568900e+01

5.304000e+00

VI

XGBOOST

Borda

4.604100e+01

3.871000e+00

VI

XGBOOST

MRMR

4.625600e+01

9.422000e+00

VI

XGBOOST

Car

4.639900e+01

7.575000e+00

HR

XGBOOST

No Filter

4.680000e+01

3.921000e+00

NRI-VI

Ridge-MBO

No Filter

1.165847e+10

2.331694e+10

HR-NRI-VI

Ridge-MBO

No Filter

1.263193e+10

2.526387e+10

HR-NRI

Ridge-MBO

No Filter

1.265012e+10

2.530024e+10

VI

Ridge-MBO

No Filter

4.935939e+10

9.871879e+10

(Table) T2 Best learner/filter/task combination

Learners: On which task and using which filter did every learner score their best result on?

*CV: L2 penalized regression using the internal 10-fold CV tuning of the glmnet package

*MBO: L2 penalized regression using using MBO for hyperparameter optimization.

Task

Model

Filter

RMSE

SE

NRI

SVM

Info Gain

27.991

19.152

NRI

RF

Car

30.774

16.861

VI

Lasso-MBO

No Filter

31.009

14.714

HR-NRI-VI

XGBOOST

Borda

31.052

17.009

NRI

Ridge-MBO

No Filter

31.165

15.025

(Table) T3 All leaner/filter/task combinations ordered descending by performance.

Overall leaderboard across all settings, sorted descending by performance.

Task

Model

Filter

RMSE

SE

VI

Ridge-MBO

No Filter

4.935939e+10

9.871879e+10

HR-NRI

Ridge-MBO

No Filter

1.265012e+10

2.530024e+10

HR-NRI-VI

Ridge-MBO

No Filter

1.263193e+10

2.526387e+10

NRI-VI

Ridge-MBO

No Filter

1.165847e+10

2.331694e+10

HR

XGBOOST

No Filter

4.680000e+01

3.921000e+00

VI

XGBOOST

Car

4.639900e+01

7.575000e+00

VI

XGBOOST

MRMR

4.625600e+01

9.422000e+00

VI

XGBOOST

Borda

4.604100e+01

3.871000e+00

VI

XGBOOST

No Filter

4.568900e+01

5.304000e+00

VI

XGBOOST

Pearson

4.461000e+01

5.783000e+00

(Plot) P1 Best learner/filter combs for all tasks

Version Author Date
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10
482a158 pat-s 2019-11-01
becf5ea pat-s 2019-11-01
bd7c7f5 pat-s 2019-10-31
62ff96f pat-s 2019-10-07
a947654 pat-s 2019-10-02
49da171 pat-s 2019-09-22
41aae14 pat-s 2019-09-12
b181c52 pat-s 2019-09-02
8e7e4fe pat-s 2019-09-01
7582c67 pat-s 2019-08-31
abd531f pat-s 2019-08-31

(Plot) P2 Scatterplots of filter methods vs. no filter for each learner and task

Showing the final effect of applying feature selection to a learner for each task. All filters are colored in the same way whereas using “no filter” appears in a different color.

Version Author Date
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10

(Plot) P3 Scatterplots of filter methods vs. Borda for each learner and task

Showing the final effect of applying feature selection to a learner for each task. All filters are summarized into a a single color whereas the “Borda” filter appears in its own color.

Version Author Date
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
776b35f pat-s 2020-03-03
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.4.0     dplyr_0.8.3       mlr_2.17.0.9001  
 [4] ParamHelpers_1.12 here_0.1          ggpubr_0.1.6     
 [7] magrittr_1.5      ggrepel_0.8.0     ggsci_2.9        
[10] ggbeeswarm_0.7.0  ggplot2_3.2.1     flextable_0.5.8  
[13] xtable_1.8-3      tidyselect_0.2.5  drake_7.12.0     

loaded via a namespace (and not attached):
 [1] fs_1.3.1           filelock_1.0.2     RColorBrewer_1.1-2
 [4] progress_1.2.0     httr_1.4.0         rprojroot_1.3-2   
 [7] tools_3.6.1        backports_1.1.5    R6_2.4.1          
[10] vipor_0.4.5        lazyeval_0.2.1     colorspace_1.4-0  
[13] withr_2.1.2        prettyunits_1.0.2  mco_1.0-15.1      
[16] compiler_3.6.1     git2r_0.26.1       parallelMap_1.4   
[19] cli_2.0.1          xml2_1.2.2         plotly_4.8.0      
[22] officer_0.3.7      labeling_0.3       scales_1.0.0      
[25] checkmate_2.0.0    plot3D_1.1.1       systemfonts_0.1.1 
[28] stringr_1.4.0      digest_0.6.23      txtq_0.1.4        
[31] rmarkdown_1.13     R.utils_2.8.0      smoof_1.5.1       
[34] base64enc_0.1-3    pkgconfig_2.0.3    htmltools_0.3.6   
[37] lhs_1.0.1          htmlwidgets_1.3    rlang_0.4.4       
[40] BBmisc_1.11        mlrMBO_1.1.2       jsonlite_1.6      
[43] zip_2.0.4          R.oo_1.23.0        Matrix_1.2-15     
[46] Rcpp_1.0.3         munsell_0.5.0      fansi_0.4.1       
[49] gdtools_0.2.1      lifecycle_0.1.0    R.methodsS3_1.7.1 
[52] stringi_1.3.1      whisker_0.3-2      yaml_2.2.0        
[55] storr_1.2.1        RJSONIO_1.3-1.1    grid_3.6.1        
[58] misc3d_0.8-4       parallel_3.6.1     promises_1.0.1    
[61] crayon_1.3.4       lattice_0.20-38    splines_3.6.1     
[64] hms_0.5.3          zeallot_0.1.0      knitr_1.23        
[67] pillar_1.4.3       igraph_1.2.4.1     uuid_0.1-2        
[70] base64url_1.4      fastmatch_1.1-0    glue_1.3.1        
[73] evaluate_0.13      data.table_1.12.8  vctrs_0.2.1       
[76] httpuv_1.4.5.1     gtable_0.3.0       purrr_0.3.3       
[79] tidyr_1.0.0        assertthat_0.2.1   xfun_0.5          
[82] later_1.0.0        survival_2.43-3    viridisLite_0.3.0 
[85] tibble_2.1.3       beeswarm_0.2.3     workflowr_1.6.1   
[88] ellipsis_0.3.0