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 penalized logistic regression model.

1 | ```
MFKnockoffs.stat.lasso_lambda_signed_max_bin(X, X_k, y, ...)
``` |

`X` |
original design matrix (size n-by-p) |

`X_k` |
knockoff matrix (size n-by-p) |

`y` |
response vector (length n). It 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). If y is presented as a vector, it will be coerced into a factor. |

`...` |
additional arguments specific to 'glmnet' or 'lars' (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`

.

This function is a wrapper around the more general MFKnockoffs.stat.glmnet_lambda_difference.

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

A vector of statistics *W* (length p)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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)
pr = 1/(1+exp(-X %*% beta))
y = rbinom(n,1,pr)
knockoffs = function(X) MFKnockoffs.create.gaussian(X, mu, Sigma)
# Basic usage with default arguments
result = MFKnockoffs.filter(X, y, knockoff=knockoffs,
statistic=MFKnockoffs.stat.lasso_lambda_signed_max_bin)
print(result$selected)
# Advanced usage with custom arguments
foo = MFKnockoffs.stat.lasso_lambda_signed_max_bin
k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200)
result = MFKnockoffs.filter(X, y, knockoffs=knockoffs, statistic=k_stat)
print(result$selected)
``` |

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