Description Usage Arguments Details Value References See Also Examples
Display results of stability selection.
1 2 3 4 5 6 
x 
object of class 
main 
main title for the plot. 
type 
plot type; either stability paths ( 
xlab, ylab 
labels for the x and yaxis of the plot. Per
default, sensible labels are used depending on the 
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 
np 
number of variables to plot for the maximum selection
frequency plot ( 
labels 
variable labels for the plot; one label per variable / effect
must be specified. Per default, the names of 
decreasing 
logical. Should the selection frequencies be printed
in descending order ( 
print.all 
logical. Should all selection frequencies be displayed or only those that are greater than zero? 
... 
additional arguments to 
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 perfamily error rate (PFER), i.e., the
expected number of false positives E(V), where V is the
number of false positives, is bounded by the argument PFER
.
As controlling the PFER is more conservative as controlling the
familywise error rate (FWER), the procedure also controlls the FWER,
i.e., the probability of selecting at least one noninfluential
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

max 
maximum of selection probabilities. 
cutoff 
cutoff used. 
q 
average number of selected variables used. 
PFER 
perfamily 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 highdimensional situations: Boosting with stability
selection. BMC Bioinformatics, 16:144.
doi: 10.1186/s1285901505753.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  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 nonsense 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")
}

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