View source: R/stats_stability_selection.R

stat.stability_selection | R Documentation |

Computes the difference statistic

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

where *Z_j* and *\tilde{Z}_j* are measure the importance
of the jth variable and its knockoff, respectively, based on the
stability of their selection upon subsampling of the data.

stat.stability_selection(X, X_k, y, fitfun = stabs::lars.lasso, ...)

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

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

`y` |
response vector (length n) |

`fitfun` |
fitfun a function that takes the arguments x, y as above, and additionally the number of variables to include in each model q. The function then needs to fit the model and to return a logical vector that indicates which variable was selected (among the q selected variables). The name of the function should be prefixed by 'stabs::'. |

`...` |
additional arguments specific to 'stabs' (see Details). |

This function uses the `stabs`

package to compute
variable selection stability. The selection stability of the j-th
variable is defined as its probability of being selected upon random
subsampling of the data. The default method for selecting variables
in each subsampled dataset is `lars.lasso`

.

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

.

A vector of statistics *W* of length p.

Other statistics:
`stat.forward_selection()`

,
`stat.glmnet_coefdiff()`

,
`stat.glmnet_lambdadiff()`

,
`stat.lasso_coefdiff_bin()`

,
`stat.lasso_coefdiff()`

,
`stat.lasso_lambdadiff_bin()`

,
`stat.lasso_lambdadiff()`

,
`stat.random_forest()`

,
`stat.sqrt_lasso()`

set.seed(2022) p=50; n=50; 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) create.gaussian(X, mu, Sigma) # Basic usage with default arguments result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=stat.stability_selection) print(result$selected)

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