# Evaluation of sparse version of SuSiE In 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 = "&nbsp;",dpi = 120)
```

## Set up environment

```library(Matrix)
library(susieR)
set.seed(1)
```

## Overview

In this vignette, we provide line profiles for revised version SuSiE, which allows for a sparse matrix structure. We compare speed performance when the form of the matrix `X` is dense and sparse.

In this minimal example, we observe that given a large sparse matrix, if it is in the dense form, the speed is around `40%` slower than that in a sparse form.

## Simulate data

We randomly simulate a `n=1000` by `p=1000` dense matrix and a sparse matrix at sparsity \$99\%\$, i.e. \$99\%\$ entries are zeros.

```create_sparsity_mat = function(sparsity, n, p) {
nonzero          <- round(n*p*(1-sparsity))
nonzero.idx      <- sample(n*p, nonzero)
mat              <- numeric(n*p)
mat[nonzero.idx] <- 1
mat              <- matrix(mat, nrow=n, ncol=p)
return(mat)
}
```
```n <- 1000
p <- 1000
beta <- rep(0,p)
beta[c(1,300,400,1000)] <- 10
X.dense  <- create_sparsity_mat(0.99,n,p)
X.sparse <- as(X.dense,"CsparseMatrix")
y <- c(X.dense %*% beta + rnorm(n))
```

## `X` in a dense form

```susie.dense <- susie(X.dense,y)
```

## `X` in a sparse form

```susie.sparse <- susie(X.sparse,y)
```

## Further step

We encourage people who are insterested in improving SuSiE can get insights from those line profiles provided.

## Try the susieR package in your browser

Any scripts or data that you put into this service are public.

susieR documentation built on March 7, 2023, 6:11 p.m.