# Using the Model-Free Knockoff Filter In MFKnockoffs: Model-Free Knockoff Filter for Controlled Variable Selection

This vignette illustrates the basic and advanced usage of MFKnockoffs.filter. For simplicity, we will use synthetic data constructed such that the response only depends on a small fraction of the variables.

set.seed(1234)

# Problem parameters
n = 1000          # number of observations
p = 1000          # 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); Sigma = diag(p)
X = matrix(rnorm(n*p),n)

# 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 MFKnockoffs.filter with all the default settings.

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


By default, the knockoff filter uses a test statistic based on the lasso. Specifically, it uses the statistic MFKnockoffs.stat.glmnet_lambda_signed_max, 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 MFKnockoffs.stat. In the next snippet, we use a statistic based on random forests. We also set a higher target FDR of 0.2.

result = MFKnockoffs.filter(X, y, knockoffs = gaussian_knockoffs, statistic = MFKnockoffs.stat.random_forest, q=0.2)
print(result)
fdp(result$selected)  ## User-defined test statistics In addition to using the predefined test statistics, it is also possible to define your own 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 = MFKnockoffs.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(...) MFKnockoffs.stat.glmnet_coef_difference(..., nlambda=100)
result = MFKnockoffs.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 equicorrelated 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) MFKnockoffs.create.approximate_gaussian(X, method='sdp', shrink=T)
result = MFKnockoffs.filter(X, y, knockoffs = gaussian_knockoffs)
print(result)


## See also

If you want to look inside the knockoff filter, see the advanced vignette. If you want to see how to use the original knockoff filter, see the fixed-design vignette.

## Try the MFKnockoffs package in your browser

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

MFKnockoffs documentation built on May 2, 2019, 6:33 a.m.