In stephenslab/susieR: Sum of Single Effects Linear Regression

knitr::opts_chunk$set(collapse = TRUE,comment = "#",fig.width = 4.5,
                      fig.height = 3,fig.align = "center",
                      fig.cap = " ",dpi = 120)

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)

Plot the ground-truth outcomes vs. the predicted outcomes:

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

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()

stephenslab/susieR documentation built on Aug. 5, 2024, 9:52 p.m.