View source: R/stats_lasso_cv_bin.R

stat.lasso_coefdiff_bin | R Documentation |

Fits a logistic regression model via penalized maximum likelihood and cross-validation. Then, compute the difference statistic

*W_j = |Z_j| - |\tilde{Z}_j|*

where *Z_j* and *\tilde{Z}_j* are the coefficient estimates for the
jth variable and its knockoff, respectively. The value of the regularization
parameter *λ* is selected by cross-validation and computed with `glmnet`

.

stat.lasso_coefdiff_bin(X, X_k, y, cores = 2, ...)

`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. |

`cores` |
Number of cores used to compute the statistics by running cv.glmnet. If not specified, the number of cores is set to approximately half of the number of cores detected by the parallel package. |

`...` |
additional arguments specific to |

This function uses the `glmnet`

package to fit the penalized logistic regression path
and is a wrapper around the more general `stat.glmnet_coefdiff`

.

The statistics *W_j* are constructed by taking the difference
between the coefficient of the j-th variable and its knockoff.

By default, the value of the regularization parameter is chosen by 10-fold cross-validation.

The optional `nlambda`

parameter can be used to control the granularity of the
grid of *λ*'s. The default value of `nlambda`

is `500`

,
where `p`

is the number of columns of `X`

.

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

and `glmnet`

.

A vector of statistics *W* of length p.

Other statistics:
`stat.forward_selection()`

,
`stat.glmnet_coefdiff()`

,
`stat.glmnet_lambdadiff()`

,
`stat.lasso_coefdiff()`

,
`stat.lasso_lambdadiff_bin()`

,
`stat.lasso_lambdadiff()`

,
`stat.random_forest()`

,
`stat.sqrt_lasso()`

,
`stat.stability_selection()`

set.seed(2022) 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, knockoffs=knockoffs, statistic=stat.lasso_coefdiff_bin) print(result$selected) # Advanced usage with custom arguments foo = stat.lasso_coefdiff_bin k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200) result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) print(result$selected)

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