Plot the stability paths of each variable (predictor), showing the selection probability as a function of the regularization step.
the SummarizedExperiment
object resulting from stability
selection, by running randLassoStabSel
.
A numerical scalar in [0,1]. Predictors with a selection
probability greater than selProbMin
are shown as colored lines. The
color is defined by the col
argument.
color of the selected predictors.
line width (default = 1).
line type (default = 1).
limits for y-axis (default = c(0,1.1)).
additional parameters to pass on to matplot
.
TRUE
(invisibly).
## 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)