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Stability selection for Quadrupen object

Source: R/quadrupen-S3Methods.R
stability.Rd

Compute the stability path of a (possibly randomized) fitting procedure as introduced by Meinshausen and Buhlmann (2010).

Usage

stability(
  object,
  n_subsamples = 50,
  subsample_size = floor(object$nobs/2),
  subsamples = replicate(n_subsamples, sample(1:object$nobs, subsample_size), simplify =
    FALSE),
  weakness = 1,
  verbose = TRUE,
  cores = 1
)

# S3 method for class 'QuadrupenFit'
stability(
  object,
  n_subsamples = 50,
  subsample_size = floor(object$nobs/2),
  subsamples = replicate(n_subsamples, sample(1:object$nobs, subsample_size), simplify =
    FALSE),
  weakness = 1,
  verbose = TRUE,
  cores = parallel::detectCores() - 2
)

Arguments

object

an R6 object with class QuadrupenFit

n_subsamples

integer indicating the number of subsamplings used to estimate the selection probabilities. Default is 100.

subsample_size

integer indicating the size of each subsamples. Default is floor(n/2).

subsamples

list with subsamples entries with vectors describing the folds to use for the stability procedure. By default, the folds are randomly sampled with the specified n_subsamples and subsample_size argument.

weakness

Coefficient used for randomizing the weights of each features. Default is 1` for no randomization. See details below.

verbose

logical; indicates if the progression should be displayed. Default is TRUE.

cores

the number of cores to use. The default uses 1 core (safer in case your BLAS/LAPACK libraries are multithreaded)

Value

an object with class StabilityPath is sent back and stored as a field of the original QuadrupenFit object.

Methods (by class)

  • stability(QuadrupenFit): S3 method for stability selection of a QuadrupenFit

Note

When weakness < 1, the penscale argument that weights the penalty tuned by \(\lambda_1\) is perturbed (divided) for each subsample by a random variable uniformly distributed on [&#945;,1], where &#945; is the weakness parameter.

If the user runs the fitting method with option 'bulletproof' set to FALSE, the algorithm may stop at an early stage of the path. Early stops of the underlying fitting function are handled internally, in the following way: we chose to simply skip the results associated with such runs, in order not to bias the stability selection procedure. If it occurs too often, a warning is sent to the user, in which case you should reconsider the grid of lambda1 for stability selection. If bulletproof is TRUE (the default), there is nothing to worry about, except a possible slow down when any switching to the proximal algorithm is required.

References

N. Meinshausen and P. Buhlmann (2010). Stability Selection, JRSS(B).

Examples

if (FALSE) { # \dontrun{
## Simulating multivariate Gaussian with blockwise correlation
## and piecewise constant vector of parameters
beta <- rep(c(0,1,0,-1,0), c(25,10,25,10,25))
Soo  <- matrix(0.75,25,25) ## bloc correlation between zero variables
Sww  <- matrix(0.75,10,10) ## bloc correlation between active variables
Sigma <- bdiag(Soo,Sww,Soo,Sww,Soo) + 0.2
diag(Sigma) <- 1
n <- 100
x <- as.matrix(matrix(rnorm(95*n),n,95) %*% chol(Sigma))
y <- 10 + x %*% beta + rnorm(n,0,10)

## Build a vector of label for true nonzeros
labels <- rep("irrelevant", length(beta))
labels[beta != 0] <- c("relevant")
labels <- factor(labels, ordered=TRUE, levels=c("relevant","irrelevant"))

enet <- elastic.net(x, y, lambda2 = 10, struct = solve(Sigma), minratio = 1e-2)
stab <- stability(enet, n_subsamples = 200)

## Build the plot an recover the selected variable
plot(stab, labels=labels)
stabpath <- plot(stab, xvar="fraction", labels=labels, sel_mode="PFER", cutoff=0.75, PFER=1)

cat("\nFalse positives for the randomized Elastic-net with stability selection: ",
     sum(labels[stab$selection()] != "relevant"))
cat("\nDONE.\n")
} # }