StabilityScore {sharp} | R Documentation |
Stability score
Description
Computes the stability score from selection proportions of models with a given parameter controlling the sparsity and for different thresholds in selection proportions. The score measures how unlikely it is that the selection procedure is uniform (i.e. uninformative) for a given combination of parameters.
Usage
StabilityScore(
selprop,
pi_list = seq(0.6, 0.9, by = 0.01),
K,
n_cat = 3,
group = NULL
)
Arguments
selprop |
array of selection proportions. |
pi_list |
vector of thresholds in selection proportions. If
|
K |
number of resampling iterations. |
n_cat |
computation options for the stability score. Default is
|
group |
vector encoding the grouping structure among predictors. This argument indicates the number of variables in each group and only needs to be provided for group (but not sparse group) penalisation. |
Details
The stability score is derived from the likelihood under the assumption of uniform (uninformative) selection.
We classify the features into three categories: the stably selected ones
(that have selection proportions ), the stably excluded ones
(selection proportion
), and the unstable ones (selection
proportions between
and
).
Under the hypothesis of equiprobability of selection (instability), the likelihood of observing stably selected, stably excluded and unstable features can be expressed as:
where is the selection count of feature
and
is the cumulative probability function of the binomial
distribution with parameters
and the average proportion of selected
features over resampling iterations.
The stability score is computed as the minus log-transformed likelihood under the assumption of equiprobability of selection:
The stability score increases with stability.
Alternatively, the stability score can be computed by considering only two
sets of features: stably selected (selection proportions ) or
not (selection proportions
). This can be done using
n_cat=2
.
Value
A vector of stability scores obtained with the different thresholds in selection proportions.
References
Bodinier B, Filippi S, Nøst TH, Chiquet J, Chadeau-Hyam M (2023). “Automated calibration for stability selection in penalised regression and graphical models.” Journal of the Royal Statistical Society Series C: Applied Statistics, qlad058. ISSN 0035-9254, doi:10.1093/jrsssc/qlad058, https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlad058/50878777/qlad058.pdf.
See Also
Other stability metric functions:
ConsensusScore()
,
FDP()
,
PFER()
,
StabilityMetrics()
Examples
# Simulating set of selection proportions
set.seed(1)
selprop <- round(runif(n = 20), digits = 2)
# Computing stability scores for different thresholds
score <- StabilityScore(selprop, pi_list = c(0.6, 0.7, 0.8), K = 100)