In this vignette, we briefly illustrate the different ways susie_rss can be called, and draw connections between running susie_rss on summary data, and running susie on individual-level data.

`library(susieR)`

Simulate a data set with 200 samples and 1,000 variables, in which the only first 4 variables affect the outcome.

```
set.seed(1)
<- 200
n <- 1000
p <- rep(0,p)
beta 1:4] <- 1
beta[<- matrix(rnorm(n*p),nrow = n,ncol = p)
X <- scale(X,center = TRUE,scale = FALSE)
X <- drop(X %*% beta + rnorm(n)) y
```

Compute summary statistics \(\hat{b}_j, \hat{s}_j\) and the correlation matrix, \({\bf R}\). These quantities will be provided as input to susie_rss.

```
<- univariate_regression(X,y)
ss <- compute_suff_stat(X,y,standardize = FALSE)
dat <- cov2cor(dat$XtX) R
```

The susie and susie_rss analyses produce the exact same results when
the summary statistics `bhat`

, `shat`

,
`var_y`

and `n`

are provided to susie_rss (and
when `R`

is an “in sample” correlation estimate—that is, when
it was computed from the same matrix `X`

that was used to
obtain the other statistics). If the covariate effects are removed from
the genotypes in univariate regression, the in-sample LD matrix should
compute from the genotype residuals where the covariate effects have
been removed.

```
<- susie(X,y,L = 10)
res1 <- susie_rss(bhat = ss$betahat,shat = ss$sebetahat,R = R,n = n,
res2 var_y = var(y),L = 10,estimate_residual_variance = TRUE)
# HINT: For estimate_residual_variance = TRUE, please check that R is the "in-sample" LD matrix; that is, the correlation matrix obtained using the exact same data matrix X that was used for the other summary statistics. Also note, when covariates are included in the univariate regressions that produced the summary statistics, also consider removing these effects from X before computing R.
plot(coef(res1),coef(res2),pch = 20,xlab = "susie",ylab = "susie_rss")
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
```

When some but not all of these statistics are provided, the results should be similar, but not exactly the same.

Next let’s compare the susie and susie_rss outputs when \({\bf X}, y\) are *standardized*
before computing the summary statistics (by “standardize”, we mean that
\(y\) and the columns of \({\bf X}\) are each divided by the sample
standard deviation so that they each have the same standard
deviation).

```
<- univariate_regression(scale(X),scale(y))
ss <- compute_suff_stat(X,y,standardize = TRUE)
dat <- cov2cor(dat$XtX) R
```

Then we compute the *z*-scores:

`<- ss$betahat/ss$sebetahat zhat `

When standardizing, providing susie_rss with summary data
`z`

(or `bhat`

, `shat`

), `R`

and `n`

is sufficient for susie_rss to recover the same
results as susie:

```
<- susie(scale(X),scale(y),L = 10)
res1 <- susie_rss(bhat = ss$betahat,shat = ss$sebetahat,R = R,n = n,
res2 L = 10,estimate_residual_variance = TRUE)
<- susie_rss(zhat,R,n = n,L = 10,estimate_residual_variance = TRUE)
res3 layout(matrix(1:2,1,2))
plot(coef(res1),coef(res2),pch = 20,xlab = "susie",
ylab = "susie_rss(bhat,shat)")
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
plot(coef(res1),coef(res3),pch = 20,xlab = "susie",ylab = "susie_rss(z)")
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
```

When the residual variance is not estimated in susie_rss, the susie_rss results may be close to susie, but may no longer be exactly the same:

```
<- susie_rss(zhat,R,n = n,L = 10)
res4 plot(coef(res1),coef(res4),pch = 20,xlab = "susie",ylab = "susie_rss")
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
```

Whenever `R`

is an “in sample” correlation matrix, we
recommend estimating the residual variance.

Without providing the sample size, `n`

, the coefficients
are interpreted as the “noncentrality parameters” (NCPs), and are
(roughly) related to the susie parameters by a factor of \(\sqrt{n}\):

```
<- susie_rss(zhat,R,L = 10)
res5 plot(coef(res1),coef(res5)/sqrt(n),pch = 20,xlab = "susie",
ylab = "susie_rss/sqrt(n)")
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")
```

Whenever possible, the sample size, or a reasonable estimate of the sample size, should be provided.