View source: R/stats_lasso_bin.R

stat.lasso_lambdasmax_bin | R Documentation |

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.

stat.lasso_lambdasmax_bin(X, X_k, y, ...)

`X` |
n-by-p matrix of original variables. |

`X_k` |
n-by-p matrix of knockoff variables. |

`y` |
vector of length n, containing the response variables. 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 |

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 `500`

.

This function is a wrapper around the more general stat.glmnet_lambdadiff.

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

.

A vector of statistics *W* of length p.

p=200; n=100; 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) create.gaussian(X, mu, Sigma) # Basic usage with default arguments result = knockoff.filter(X, y, knockoff=knockoffs, statistic=stat.lasso_lambdasmax_bin) print(result$selected) # Advanced usage with custom arguments foo = stat.lasso_lambdasmax_bin k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200) result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) print(result$selected)

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