MFKnockoffs.stat.glmnet_lambda_signed_max: GLM statistics for MFKnockoffs
In MFKnockoffs: Model-Free Knockoff Filter for Controlled Variable Selection

Description Usage Arguments Details Value Examples

Computes the signed maximum statistic

W_j = \max(Z_j, \tilde{Z}_j) \cdot \mathrm{sgn}(Z_j - \tilde{Z}_j),

where Z_j and \tilde{Z}_j are the maximum values of λ at which the jth variable and its knockoff, respectively, enter the generalized linear model.

1	MFKnockoffs.stat.glmnet_lambda_signed_max(X, X_k, y, family = "gaussian", ...)

`X`	original design matrix (size n-by-p)
`X_k`	knockoff matrix (size n-by-p)
`y`	response vector (length n). Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For either "binomial" or "multinomial", if y is presented as a vector, it will be coerced into a factor. For family="cox", y should be a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function Surv() in package survival produces such a matrix. For family="mgaussian", y is a matrix of quantitative responses.
`family`	Response type (see above)
`...`	additional arguments specific to 'glmnet' (see Details)

This function uses glmnet to compute the regularization path on a fine grid of λ's.

The additional nlambda parameter can be used to control the granularity of the grid of λ values. The default value of nlambda is 100.

If the family is 'binomial' and a lambda sequence is not provided by the user, this function generates it on a log-linear scale before calling 'glmnet'.

For a complete list of the available additional arguments, see glmnet.

A vector of statistics W (length p)

p=100; n=200; k=15
mu = rep(0,p); Sigma = diag(p)
X = matrix(rnorm(n*p),n)
nonzero = sample(p, k)
beta = 3.5 * (1:p %in% nonzero)
y = X %*% beta + rnorm(n)

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

# Advanced usage with custom arguments
foo = MFKnockoffs.stat.glmnet_lambda_signed_max
k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200)
result = MFKnockoffs.filter(X, y, knockoffs=knockoffs, statistic=k_stat)
print(result$selected)