Permute residuals for a fitted ncvreg model

Fits multiple penalized regression models in which the residuals are randomly permuted, thereby allowing estimation of the marginal false discovery rate.

Usage

permres(fit, ...)

# S3 method for class 'ncvreg'
permres(fit, lambda, N = 10, seed, trace = FALSE, ...)

Arguments

fit: A fitted ncvreg model, as produced by ncvreg(). To use with permres, the model must be fit using the returnX=TRUE option.
...: Not used.
lambda: The regularization parameter to use for estimating residuals. Unlike perm.ncvreg(), permres() calculates EF and mFDR for a specific lambda value, not an entire path. As a result, it runs much faster.
N: The number of permutation replications. Default is 10.
seed: You may set the seed of the random number generator in order to obtain reproducible results.
trace: If set to TRUE, perm.ncvreg will inform the user of its progress by announcing the beginning of each permutation fit. Default is FALSE.

Value

A list with the following components:

EF: The number of variables selected at each value of lambda, averaged over the permutation fits.
S: The actual number of selected variables for the non-permuted data.
mFDR: The estimated marginal false discovery rate (EF/S).
loss: The loss/deviance, averaged over the permutation fits. This is an estimate of the explanatory power of the model under null conditions, and can be used to adjust the loss of the fitted model in a manner akin to the idea of an adjusted R-squared in classical regression.

Details

The function fits a penalized regression model to the actual data, then repeats the process N times with a permuted version of the response vector. This allows estimation of the expected number of variables included by chance for each value of lambda. The ratio of this expected quantity to the number of selected variables using the actual (non-permuted) response is called the marginal false discovery rate (mFDR).

Author

Patrick Breheny patrick-breheny@uiowa.edu

Examples

data(Prostate)
fit <- ncvreg(Prostate$X, Prostate$y, N=50)
permres(fit, lambda=0.15)
#> $EF
#> [1] 0.3
#> 
#> $S
#> 0.1500 
#>      3 
#> 
#> $mFDR
#> 0.1500 
#>    0.1 
#> 
#> $loss
#> [1] 48.63362
#>