Nothing

```
## ---- results='hide', message=FALSE, warning=FALSE----------------------------
set.seed(1234)
## -----------------------------------------------------------------------------
# Problem parameters
n = 1000 # number of observations
p = 300 # number of variables
k = 30 # number of variables with nonzero coefficients
amplitude = 4.5 # signal amplitude (for noise level = 1)
# Generate the variables from a multivariate normal distribution
mu = rep(0,p)
rho = 0.25
Sigma = toeplitz(rho^(0:(p-1)))
X = matrix(rnorm(n*p),n) %*% chol(Sigma)
# Generate the response from a linear model
nonzero = sample(p, k)
beta = amplitude * (1:p %in% nonzero) / sqrt(n)
y.sample = function(X) X %*% beta + rnorm(n)
y = y.sample(X)
## ---- results='hide', message=FALSE-------------------------------------------
library(knockoff)
result = knockoff.filter(X, y, knockoffs = create.fixed, statistic = stat.glmnet_lambdasmax)
## -----------------------------------------------------------------------------
print(result)
## -----------------------------------------------------------------------------
fdp = function(selected) sum(beta[selected] == 0) / max(1, length(selected))
fdp(result$selected)
```

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