# create.gaussian: Model-X Gaussian knockoffs In knockoff: The Knockoff Filter for Controlled Variable Selection

 create.gaussian R Documentation

## Model-X Gaussian knockoffs

### Description

Samples multivariate Gaussian model-X knockoff variables.

### Usage

```create.gaussian(X, mu, Sigma, method = c("asdp", "sdp", "equi"), diag_s = NULL)
```

### Arguments

 `X` n-by-p matrix of original variables. `mu` vector of length p, indicating the mean parameter of the Gaussian model for X. `Sigma` p-by-p covariance matrix for the Gaussian model of X. `method` either "equi", "sdp" or "asdp" (default: "asdp"). This determines the method that will be used to minimize the correlation between the original variables and the knockoffs. `diag_s` vector of length p, containing the pre-computed covariances between the original variables and the knockoffs. This will be computed according to `method`, if not supplied.

### Value

A n-by-p matrix of knockoff variables.

### References

Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://web.stanford.edu/group/candes/knockoffs/index.html

Other create: `create.fixed()`, `create.second_order()`

### Examples

```set.seed(2022)
p=100; n=80; k=15
rho = 0.4
mu = rep(0,p); Sigma = toeplitz(rho^(0:(p-1)))
X = matrix(rnorm(n*p),n) %*% chol(Sigma)
nonzero = sample(p, k)
beta = 3.5 * (1:p %in% nonzero)
y = X %*% beta + rnorm(n)

# Basic usage with default arguments
knockoffs = function(X) create.gaussian(X, mu, Sigma)
result = knockoff.filter(X, y, knockoffs=knockoffs)
print(result\$selected)

# Advanced usage with custom arguments
knockoffs = function(X) create.gaussian(X, mu, Sigma, method='equi')
result = knockoff.filter(X, y, knockoffs=knockoffs)
print(result\$selected)

```

knockoff documentation built on Aug. 15, 2022, 9:06 a.m.