selection: Generalized Model-Free Knockoff Filter for Controlled...
In hanfu-bios/varsel: Model-Free Knockoffs Filter for Controlled Variable Selection

Description Usage Arguments Details Value References

This function is the main entry point for the package. It runs the knockoff procedure which accomodates various covariate distributions and model-free associations using gradient boosting, and ultimately provides the selected variables for the input dataset. Users can specify whether to screen variables before selection and the extent of screening.

knockoffs.varsel(X, y, X_k, family = Gaussian(),
  q=0.10, knockoff.method = c("sdp","asdp","svm"), knockoff.shrink = T,
  stat = c("RRB", "LCD", "DRS"),
  screen = T, screening.num = nrow(X), screening.knot = 10,
  max.mstop = 100, baselearner = c("bbs", "bols", "btree"), cv.fold = 5,
  threshold=c('knockoff','knockoff+')

`X`	matrix of predictors
`y`	response vector, or a survival object with two columns
`X_k`	knockoff variables (n by p), if pre-specified
`family`	Binomial(), Binomial(link = “logit”, type=”glm”), Gaussian(), Poisson(), CoxPH(), Cindex(), GammaReg(), NBinomial(), Weibull(), Loglog(), Lognormal(), etc. See mboost documentation for details.
`q`	target FDR (false discovery rate)
`knockoff.method`	method to construct knockoffs. 'sdp' or 'asdp' means sampling second-order multivariate Gaussian knockoffs via either SDP or approximate SDP. 'svm' means constructing by regression, specifically, by support vector regression
`knockoff.shrink`	whether to shrink the estimated covariance matrix (default: FALSE)
`stat`	statistics measuring variable importance. 'RRB' represents risk reduction in boosting, 'LCD' represents Lasso coefficient difference, and 'DRS' represents difference in R-square (R-squares are obtained from boosting)
`screen`	whether to screen the variables (default: TRUE)
`screening.num`	number of variables left after screening (default: sample size)
`screening.knot`	parameter in screening process
`max.mstop`	maximum number of boosting iteration
`baselearner`	base-learners when fitting models using mboost. 'bols' means linear base-learners, 'bbs' penalized regression splines with a B-spline basis, and 'btree' boosts stumps.
`cv.fold`	number of folds in cross-validation to choose number of iteration
`threshold`	method to calculate knockoff threshold, either “knockoff” or “knockoff+”

The default family for continuous response is Gaussian(), Binomial() for binary response, and CoxPH() for survival response.

A vector containing the selected covariate indices

Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html

Barber and Candes, Controlling the false discovery rate via knockoffs. Ann. Statist. 43 (2015), no. 5, 2055–2085. https://projecteuclid.org/euclid.aos/1438606853

Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid (2014). Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost. Computational Statistics, 29, 3–35. http://dx.doi.org/10.1007/s00180-012-0382-5 Available as vignette via: vignette(package = "mboost", "mboost_tutorial")

hanfu-bios/varsel documentation built on March 19, 2018, 10:08 a.m.