stability: Function to perform stability selection
In hdi: High-Dimensional Inference

Description Usage Arguments Value Author(s) References Examples

Function to perform stability selection

stability(x, y, EV, threshold = 0.75, B = 100, fraction = 0.5,
          model.selector = lasso.firstq, args.model.selector = NULL,
          parallel = FALSE, ncores = getOption("mc.cores", 2L),
          verbose = FALSE)

`x`	Design matrix (without intercept).
`y`	Response vector.
`EV`	Bound for expected number of false positives.
`threshold`	Threshold for selection frequency. Must be in (0.5, 1).
`B`	Number of sub-sample iterations.
`fraction`	Fraction of data used at each of the B sub-samples.
`model.selector`	Function to perform model selection. Default is `lasso.firstq`. User supplied function must have at least three arguments: x (the design matrix), y (the response vector) and q (the maximal model size). Return value is the index vector of selected columns. See `lasso.firstq` for an example. Additional arguments can be passed through `args.model.selector`.
`args.model.selector`	Named list of further arguments for function `model.selector`.
`parallel`	Should parallelization be used? (logical)
`ncores`	Number of cores used for parallelization.
`verbose`	Should information be printed out while computing (logical).

`selected`	Vector of selected predictors.
`freq`	Vector of selection frequencies.
`q`	Size of fitted models in order to control error rate at desired level.

Lukas Meier

Meinshausen, N. and Bühlmann, P. (2010) Stability selection (with discussion). Journal of the Royal Statistical Society: Series B 72, 417–473.

Bühlmann, P., Kalisch, M. and Meier, L. (2014) High-dimensional statistics with a view towards applications in biology. Annual Review of Statistics and its Applications 1, 255–278

x <- matrix(rnorm(100 * 1000), nrow = 100, ncol = 1000)
y <- x[,1] * 2 + x[,2] * 2.5 + rnorm(100)
fit.stab <- stability(x, y, EV = 1)
fit.stab
fit.stab$freq[1:10] ## selection frequency of the first 10 predictors