Plot and Print Methods for Stability Selection
Display results of stability selection.
## S3 method for class 'stabsel' plot(x, main = deparse(x$call), type = c("maxsel", "paths"), xlab = NULL, ylab = NULL, col = NULL, ymargin = 10, np = sum(x$max > 0), labels = NULL, ...) ## S3 method for class 'stabsel' print(x, decreasing = FALSE, print.all = TRUE, ...)
x: object of class stabsel.
main: main title for the plot.
type: plot type; either stability paths ("paths") or a plot of the maximum selection frequency ("maxsel").
xlab, ylab: labels for the x- and y-axis of the plot. Per default, sensible labels are used depending on the type of the plot.
col: a vector of colors; Typically, one can specify a single color or one color for each variable. Per default, colors depend on the maximal selection frequency of the variable and range from grey to red.
ymargin: (temporarily) specifies the y margin of of the plot in lines (see argument "mar" of function par). This only affects the right margin for type = "paths" and the left margin for type = "maxsel". Explicit user specified margins are kept and are not overwritten.
np: number of variables to plot for the maximum selection frequency plot (type = "maxsel"); the first np
variables with highest selection frequency are plotted.
labels: variable labels for the plot; one label per variable / effect must be specified. Per default, the names of x$max are used.
decreasing: logical. Should the selection frequencies be printed in descending order (TRUE) or in ascending order (FALSE)?
print.all: logical. Should all selection frequencies be displayed or only those that are greater than zero?
...: additional arguments to plot and print
functions.
This function implements the stability selection procedure by Meinshausen and Buehlmann (2010) and the improved error bounds by Shah and Samworth (2013).
Two of the three arguments cutoff, q and PFER
must be specified. The per-family error rate (PFER), i.e., the expected number of false positives , where is the number of false positives, is bounded by the argument PFER.
As controlling the PFER is more conservative as controlling the family-wise error rate (FWER), the procedure also controlls the FWER, i.e., the probability of selecting at least one non-influential variable (or model component) is less than PFER.
An object of class stabsel with a special print method. The object has the following elements: - phat: selection probabilities.
selected: elements with maximal selection probability greater cutoff.
max: maximum of selection probabilities.
cutoff: cutoff used.
q: average number of selected variables used.
PFER: per-family error rate.
sampling.type: the sampling type used for stability selection.
assumption: the assumptions made on the selection probabilities.
call: the call.
B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false discoveries in high-dimensional situations: Boosting with stability selection. BMC Bioinformatics, 16:144.
tools:::Rd_expr_doi("10.1186/s12859-015-0575-3") .
N. Meinshausen and P. Buehlmann (2010), Stability selection. Journal of the Royal Statistical Society, Series B, 72 , 417--473.
R.D. Shah and R.J. Samworth (2013), Variable selection with error control: another look at stability selection. Journal of the Royal Statistical Society, Series B, 75 , 55--80.
stabsel
if (require("TH.data")) { ## make data set available data("bodyfat", package = "TH.data") } else { ## simulate some data if TH.data not available. ## Note that results are non-sense with this data. bodyfat <- matrix(rnorm(720), nrow = 72, ncol = 10) } ## set seed set.seed(1234) #################################################################### ### using stability selection with Lasso methods: if (require("lars")) { (stab.lasso <- stabsel(x = bodyfat[, -2], y = bodyfat[,2], fitfun = lars.lasso, cutoff = 0.75, PFER = 1)) par(mfrow = c(2, 1)) plot(stab.lasso, ymargin = 6) opar <- par(mai = par("mai") * c(1, 1, 1, 2.7)) plot(stab.lasso, type = "paths") }