Plot the stability paths of each variable (predictor), showing the selection probability as a function of the regularization step.
Arguments
- se
The
SummarizedExperiment
object resulting from stability selection, by runningrandLassoStabSel
.- selProbMin
A numerical scalar in [0,1]. Predictors with a selection probability greater than
selProbMin
are shown as colored lines. The color is defined by thecol
argument.- selColor
Color for the selected predictors which have a selection probability greater than
selProbMin
.- notSelColor
Color for the rest of the (un-selected) predictors.
- selProbCutoffColor
Color for the line depicting the selection probability cutoff.
- linewidth
Line width.
- alpha
Line transparency of the stability paths.
- ylim
Limits for y-axis.
- labelPaths
If
TRUE
, the predictor labels will be shown at the end of the stability paths. The predictor labels given inlabels
will be shown. If unspecified, the labels corresponding to the selected predictors will be added. If predictors have the same y-value in the last regularization step, the labels will be shown in a random order. One needs to useset.seed
to reproduce the plot in this case.- labels
If
labelPaths = TRUE
, the predictors which should be labelled. IfNULL
, the selected predictors greater thanmetadata(se)$stabsel.params.cutoff
will be shown.- labelNudgeX
If
labelPaths = TRUE
, how much to nudge the labels to the right of the x-axis.- labelSize
If
labelPaths = TRUE
, the size of the labels.
Examples
## create data set
Y <- rnorm(n = 500, mean = 2, sd = 1)
X <- matrix(data = NA, nrow = length(Y), ncol = 50)
for (i in seq_len(ncol(X))) {
X[ ,i] <- runif(n = 500, min = 0, max = 3)
}
s_cols <- sample(x = seq_len(ncol(X)), size = 10,
replace = FALSE)
for (i in seq_along(s_cols)) {
X[ ,s_cols[i]] <- X[ ,s_cols[i]] + Y
}
## reproducible randLassoStabSel() with 1 core
set.seed(123)
ss <- randLassoStabSel(x = X, y = Y)
plotStabilityPaths(ss)