Fits multiple penalized regression models in which the residuals are randomly permuted, thereby allowing estimation of the marginal false discovery rate.
permres(fit, ...)# S3 method for ncvreg
permres(fit, lambda, N = 10, seed, trace = FALSE, ...)
A list with the following components:
The number of variables selected at each value of lambda, averaged over the permutation fits.
The actual number of selected variables for the non-permuted data.
The estimated marginal false discovery rate (EF/S).
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.
A fitted ncvreg model, as produced by ncvreg(). To use with
permres, the model must be fit using the returnX=TRUE option.
Not used.
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.
The number of permutation replications. Default is 10.
You may set the seed of the random number generator in order to obtain reproducible results.
If set to TRUE, perm.ncvreg will inform the user of its progress by announcing the beginning of each permutation fit. Default is FALSE.
Patrick Breheny patrick-breheny@uiowa.edu
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).
ncvreg(), `mfdr(), perm.ncvreg()
data(Prostate)
fit <- ncvreg(Prostate$X, Prostate$y, N=50)
permres(fit, lambda=0.15)
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