quadrupen (version 0.2-4)

stability: Stability selection for a quadrupen fit.

Description

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

Usage

stability(x, y, penalty = c("elastic.net", "bounded.reg"), subsamples = 100, sample.size = floor(n/2), randomize = TRUE, weakness = 0.5, verbose = TRUE, folds = replicate(subsamples, sample(1:nrow(x), sample.size), simplify = FALSE), mc.cores = detectCores(), ...)

Arguments

x

matrix of features, possibly sparsely encoded (experimental). Do NOT include intercept.

y

response vector.

penalty

a string for the fitting procedure used for cross-validation. Either elastic.net or "bounded.reg".

subsamples

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

sample.size

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

randomize

Should a randomized version of the fitting procedure by used? Default is TRUE. See details below.

weakness

Coefficient used for randomizing. Default is 0.5. Ignored when randomized is FALSE. See details below.

folds

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 subsamples argument.

verbose

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

mc.cores

the number of cores to use. The default uses all the cores available.

...

additional parameters to overwrite the defaults of the fitting procedure. See the corresponding documentation (elastic.net or bounded.reg)

Value

An object of class stability.path.

References

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

See Also

stability.path and plot,stability.path-method.

Examples

## Not run: 
# ## 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"))
# 
# ## Call to stability selection function, 200 subsampling
# stab <- stability(x,y, subsamples=200, lambda2=1, min.ratio=1e-2)
# ## Recover the selected variables for a given cutoff
# ## and per-family error rate, without producing any plot
# stabpath <- plot(stab, cutoff=0.75, PFER=1, plot=FALSE)
# 
# cat("\nFalse positives for the randomized Elastic-net with stability selection: ",
#      sum(labels[stabpath$selected] != "relevant"))
# cat("\nDONE.\n")
# ## End(Not run)