A minimal example

Matthew Stephens

2021-11-12

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_0.11.92
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.7         highr_0.8          plyr_1.8.5         compiler_3.6.2    
#  [5] pillar_1.6.2       tools_3.6.2        digest_0.6.23      evaluate_0.14     
#  [9] lifecycle_1.0.0    tibble_3.1.3       gtable_0.3.0       lattice_0.20-38   
# [13] pkgconfig_2.0.3    rlang_0.4.11       Matrix_1.2-18      DBI_1.1.0         
# [17] parallel_3.6.2     yaml_2.2.0         xfun_0.11          stringr_1.4.0     
# [21] dplyr_1.0.7        knitr_1.26         generics_0.0.2     vctrs_0.3.8       
# [25] RcppZiggurat_0.1.5 Rfast_2.0.3        grid_3.6.2         tidyselect_1.1.1  
# [29] reshape_0.8.8      glue_1.4.2         R6_2.4.1           fansi_0.4.0       
# [33] rmarkdown_2.3      mixsqp_0.3-46      irlba_2.3.3        reshape2_1.4.3    
# [37] ggplot2_3.3.5      purrr_0.3.4        magrittr_2.0.1     matrixStats_0.61.0
# [41] scales_1.1.0       htmltools_0.4.0    ellipsis_0.3.2     assertthat_0.2.1  
# [45] colorspace_1.4-1   utf8_1.1.4         stringi_1.4.3      munsell_0.5.0     
# [49] crayon_1.4.1