| plotStabilityPaths {monaLisa} | R Documentation |
Plot the stability paths of each variable (predictor), showing the selection probability as a function of the regularization step.
plotStabilityPaths( se, selProbMin = metadata(se)$stabsel.params.cutoff, col = "cadetblue", lwd = 1, lty = 1, ylim = c(0, 1.1), ... )
se |
the |
selProbMin |
A numerical scalar in [0,1]. Predictors with a selection
probability greater than |
col |
color of the selected predictors. |
lwd |
line width (default = 1). |
lty |
line type (default = 1). |
ylim |
limits for y-axis (default = c(0,1.1)). |
... |
additional parameters to pass on to |
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)