View source: R/stats_glmnet_cv.R

stat.glmnet_coefdiff | R Documentation |

Fits a generalized linear 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.glmnet_coefdiff(X, X_k, y, family = "gaussian", 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. 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). |

`cores` |
Number of cores used to compute the statistics by running cv.glmnet. Unless otherwise specified, the number of cores is set equal to two (if available). |

`...` |
additional arguments specific to |

This function uses the `glmnet`

package to fit a generalized linear model
via penalized maximum likelihood.

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 default response family is 'gaussian', for a linear regression model. Different response families (e.g. 'binomial') can be specified by passing an optional parameter 'family'.

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`

.

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 `cv.glmnet`

and `glmnet`

.

A vector of statistics *W* of length p.

Other statistics:
`stat.forward_selection()`

,
`stat.glmnet_lambdadiff()`

,
`stat.lasso_coefdiff_bin()`

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

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