Reproduce Results of Gerard and Hoff (2017)

This folder contains the code necessary to reproduce the results of Gerard and Hoff (2017). To reproduce these results you will need to:

1. Download the appropriate R packages.
2. Run make.
3. Get some coffee.

Download the appropriate R packages.

You can obtain all of the needed R packages by running the following code in R:

install.packages(c("dplyr", "ggplot2", "tidyr", "xtable", 
                   "devtools", "snow", "cate", "ggthemes",
                   "stringr"))
source("https://bioconductor.org/biocLite.R")
biocLite("sva")
devtools::install_github("dcgerard/tensr")
devtools::install_github("dcgerard/hose")

To download the code in this repo, click on this link.

Run make

To reproduce all of the results of Gerard and Hoff (2017), simply run make from the terminal (not in the R session). To reproduce the figure from Section 2, run in the terminal:

make change_sv

To reproduce the simulation results from the paper, run in the terminal:

make sims

To reproduce the analysis of NBA statistics, run in the terminal:

make nba

Get coffee

Some of the simulations will take awhile to run (2 to 10 hours depending on how many cores you are using). You should get some coffee! Here is a list of some of my favorite places:

• Chicago
  • Sawada Coffee
  • Plein Air Cafe
• Seattle
  • Bauhaus Ballard
  • Cafe Solstice
• Columbus
  • Yeah, Me Too
  • Stauf’s Coffee Roasters
  • Caffe Apropos

Bugs

If you have trouble running this code, then it might be that you need to update your R packages. I ran these simulations under these settings:

sessionInfo()
#> R version 3.6.1 (2019-07-05)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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] stringr_1.4.0       ggthemes_4.2.0      cate_1.1           
#>  [4] sva_3.32.1          BiocParallel_1.18.1 genefilter_1.66.0  
#>  [7] mgcv_1.8-28         nlme_3.1-141        snow_0.4-3         
#> [10] xtable_1.8-4        tidyr_1.0.0         ggplot2_3.2.1      
#> [13] tensr_1.0.1         hose_1.0.0          dplyr_0.8.3        
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.2           lattice_0.20-38      esaBcv_1.2.1        
#>  [4] corpcor_1.6.9        assertthat_0.2.1     zeallot_0.1.0       
#>  [7] digest_0.6.20        R6_2.4.0             backports_1.1.4     
#> [10] stats4_3.6.1         RSQLite_2.1.2        evaluate_0.14       
#> [13] pillar_1.4.2         rlang_0.4.0          svd_0.5             
#> [16] lazyeval_0.2.2       annotate_1.62.0      blob_1.2.0          
#> [19] S4Vectors_0.22.1     Matrix_1.2-17        rmarkdown_1.15      
#> [22] splines_3.6.1        RCurl_1.95-4.12      bit_1.1-14          
#> [25] munsell_0.5.0        compiler_3.6.1       xfun_0.9            
#> [28] pkgconfig_2.0.2      BiocGenerics_0.30.0  ruv_0.9.7.1         
#> [31] htmltools_0.3.6      tidyselect_0.2.5     gridExtra_2.3       
#> [34] tibble_2.1.3         IRanges_2.18.2       matrixStats_0.55.0  
#> [37] leapp_1.2            XML_3.98-1.20        crayon_1.3.4        
#> [40] withr_2.1.2          MASS_7.3-51.4        bitops_1.0-6        
#> [43] grid_3.6.1           gtable_0.3.0         lifecycle_0.1.0     
#> [46] DBI_1.0.0            magrittr_1.5         scales_1.0.0        
#> [49] stringi_1.4.3        limma_3.40.6         vctrs_0.2.0         
#> [52] tools_3.6.1          bit64_0.9-7          Biobase_2.44.0      
#> [55] glue_1.3.1           purrr_0.3.2          parallel_3.6.1      
#> [58] survival_2.44-1.1    yaml_2.2.0           AnnotationDbi_1.46.1
#> [61] colorspace_1.4-1     memoise_1.1.0        knitr_1.24

As you can see above, I’ve only tried this on Linux.

If you still have difficulty, please submit an issue.

References

Gerard, David, and Peter Hoff. 2017. “Adaptive Higher-Order Spectral Estimators.” Electron. J. Statist. 11 (2): 3703–37. https://doi.org/10.1214/17-EJS1330.