Skip to content

Plot Empirical FDR

plot_emp_fdr.Rd

Plot the mean number of false positives (FPs) versus the number of selected features by a sequence of selection probability thresholds. For this to be possible, the stab_sel object must have permuted data in order to calculate empirical false discovery rates. The area in the sub-diagonal represents where more features are added without a commensurate increase in false positives (Good). The inverse is true for the super-diagonal, false positives are being included faster than additional features (Bad). The legend highlights pre-defined empirical FDR breaks: c(0.5, 1, 2, 3, 5) evaluated to the nearest threshold cutoff.

Usage

plot_emp_fdr(x, thresh_seq = seq(1, 0.1, by = -0.01))

plot_permuted_data(x, which = NULL, ...)

Arguments

x

A stab_sel class object generated via stability_selection().

thresh_seq

numeric(n). A sequence in [0, 1] specifying the thresholds to evaluate.

which

integer(1). Which of the null hypothesis permuted stability paths to plot.

...

Additional arguments passed to plot.stab_sel().

Functions

  • plot_permuted_data(): Plot the permutation paths for an object of class stab_sel. These paths are the stability selection paths of the n class scrambled permutations, i.e. the null.

See also

stability_selection(), get_stable_features()

Other empirical FDR: calc_emp_fdr(), calc_emp_fdr_breaks()

Author

Stu Field

Examples

# l1-logistic
withr::with_seed(101, {
  n_feat      <- 20
  n_samples   <- 100
  x           <- matrix(rnorm(n_feat * n_samples), n_samples, n_feat)
  colnames(x) <- paste0("feat", "_", head(letters, n_feat))
  y           <- sample(1:2, n_samples, replace = TRUE)
})

# typically set > 75 permutations
stab_sel <- stability_selection(x, y, "l1-logistic", num_perms = 25,
                                r_seed = 101, parallel = TRUE)
#> ✓ Using kernel: 'l1-logistic' and 1 core (serial)
plot_emp_fdr(stab_sel)
#> Warning: No shared levels found between `names(values)` of the manual scale and
#> the data's colour values.
#> Warning: No shared levels found between `names(values)` of the manual scale and
#> the data's colour values.


# Plot the permuted data individually
plot_permuted_data(stab_sel, 3L)   # choose 3rd permutation