A minimal example

Matthew Stephens

2022-09-05

In this short vignette, we fit a sparse linear regression model with up to \(L > 0\) non-zero effects. Generally, there is no harm in over-stating \(L\) (that is, the method is pretty robust to overfitting), except that computation will grow as \(L\) grows.

Here is a minimal example:

library(susieR)
set.seed(1)
n    <- 1000
p    <- 1000
beta <- rep(0,p)
beta[c(1,2,300,400)] <- 1
X   <- matrix(rnorm(n*p),nrow=n,ncol=p)
y   <- X %*% beta + rnorm(n)
res <- susie(X,y,L=10)
plot(coef(res)[-1],pch = 20)
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Plot the ground-truth outcomes vs. the predicted outcomes:

plot(y,predict(res),pch = 20)
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Session information

Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results.

sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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     
# 
# other attached packages:
# [1] L0Learn_1.2.0  susieR_0.12.27
# 
# loaded via a namespace (and not attached):
#  [1] tidyselect_1.1.1   xfun_0.29          bslib_0.3.1        reshape2_1.4.3    
#  [5] purrr_0.3.4        lattice_0.20-38    colorspace_1.4-1   vctrs_0.3.8       
#  [9] generics_0.0.2     htmltools_0.5.2    yaml_2.2.0         utf8_1.1.4        
# [13] rlang_0.4.11       mixsqp_0.3-46      jquerylib_0.1.4    pillar_1.6.2      
# [17] glue_1.4.2         DBI_1.1.0          RcppZiggurat_0.1.5 matrixStats_0.61.0
# [21] lifecycle_1.0.0    plyr_1.8.5         stringr_1.4.0      munsell_0.5.0     
# [25] gtable_0.3.0       evaluate_0.14      knitr_1.37         fastmap_1.1.0     
# [29] irlba_2.3.3        parallel_3.6.2     fansi_0.4.0        Rfast_2.0.3       
# [33] highr_0.8          Rcpp_1.0.8         scales_1.1.0       jsonlite_1.7.2    
# [37] ggplot2_3.3.6      digest_0.6.23      stringi_1.4.3      dplyr_1.0.7       
# [41] grid_3.6.2         tools_3.6.2        magrittr_2.0.1     sass_0.4.0        
# [45] tibble_3.1.3       crayon_1.4.1       pkgconfig_2.0.3    ellipsis_0.3.2    
# [49] Matrix_1.4-2       assertthat_0.2.1   rmarkdown_2.11     reshape_0.8.8     
# [53] R6_2.4.1           compiler_3.6.2