# Controlled variable Selection with Model-X Knockoffs In knockoff: The Knockoff Filter for Controlled Variable Selection

This vignette illustrates the basic usage of the knockoff package with Model-X knockoffs. In this scenario we assume that the distribution of the predictors is known (or that it can be well approximated), but we make no assumptions on the conditional distribution of the response. For simplicity, we will use synthetic data constructed from a linear model such that the response only depends on a small fraction of the variables.

set.seed(1234)

# Problem parameters
n = 200           # number of observations
p = 200           # number of variables
k = 60            # 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)


## First examples

To begin, we call knockoff.filter with all the default settings.

library(knockoff)
result = knockoff.filter(X, y)


We can display the results with

print(result)


The default value for the target false discovery rate is 0.1. In this experiment the false discovery proportion is

fdp = function(selected) sum(beta[selected] == 0) / max(1, length(selected))
fdp(result$selected)  By default, the knockoff filter creates model-X second-order Gaussian knockoffs. This construction estimates from the data the mean$\mu$and the covariance$\Sigma$of the rows of$X$, instead of using the true parameters ($\mu, \Sigma$) from which the variables were sampled. The knockoff package also includes other knockoff construction methods, all of which have names prefixed withknockoff.create. In the next snippet, we generate knockoffs using the true model parameters. gaussian_knockoffs = function(X) create.gaussian(X, mu, Sigma) result = knockoff.filter(X, y, knockoffs=gaussian_knockoffs) print(result)  Now the false discovery proportion is fdp(result$selected)


By default, the knockoff filter uses a test statistic based on the lasso. Specifically, it uses the statistic stat.glmnet_coefdiff, which computes $$W_j = |Z_j| - |\tilde{Z}_j|$$ where $Z_j$ and $\tilde{Z}_j$ are the lasso coefficient estimates for the jth variable and its knockoff, respectively. The value of the regularization parameter $\lambda$ is selected by cross-validation and computed with glmnet.

Several other built-in statistics are available, all of which have names prefixed with stat. For example, we can use statistics based on random forests. In addition to choosing different statistics, we can also vary the target FDR level (e.g. we now increase it to 0.2).

result = knockoff.filter(X, y, knockoffs = gaussian_knockoffs, statistic = stat.random_forest, fdr=0.2)
print(result)
fdp(result$selected)  ## User-defined test statistics In addition to using the predefined test statistics, it is also possible to use your own custom test statistics. To illustrate this functionality, we implement one of the simplest test statistics from the original knockoff filter paper, namely $$W_j = \left|X_j^\top \cdot y\right| - \left|\tilde{X}_j^\top \cdot y\right|.$$ my_knockoff_stat = function(X, X_k, y) { abs(t(X) %*% y) - abs(t(X_k) %*% y) } result = knockoff.filter(X, y, knockoffs = gaussian_knockoffs, statistic = my_knockoff_stat) print(result) fdp(result$selected)


As another example, we show how to customize the grid of $\lambda$'s used to compute the lasso path in the default test statistic.

my_lasso_stat = function(...) stat.glmnet_coefdiff(..., nlambda=100)
result = knockoff.filter(X, y, knockoffs = gaussian_knockoffs, statistic = my_lasso_stat)
print(result)


## Approximate vs Full SDP knockoffs

The knockoff package supports two main styles of knockoff variables, semidefinite programming (SDP) knockoffs (the default) and equi-correlated knockoffs. Though more computationally expensive, the SDP knockoffs are statistically superior by having higher power. To create SDP knockoffs, this package relies on the R library [Rdsdp][Rdsdp] to efficiently solve the semidefinite program. In high-dimensional settings, this program becomes computationally intractable. A solution is then offered by approximate SDP (ASDP) knockoffs, which address this issue by solving a simpler relaxed problem based on a block-diagonal approximation of the covariance matrix. By default, the knockoff filter uses SDP knockoffs if $p<500$ and ASDP knockoffs otherwise.

In this example we generate second-order Gaussian knockoffs using the estimated model parameters and the full SDP construction. Then, we run the knockoff filter as usual.

gaussian_knockoffs = function(X) create.second_order(X, method='sdp', shrink=T)
result = knockoff.filter(X, y, knockoffs = gaussian_knockoffs)
print(result)