dormancy_depth: Measure the Depth of Dormancy in Patterns

In dormancy: Detection and Analysis of Dormant Patterns in Data

Measure the Depth of Dormancy in Patterns

Description

Quantifies how "deeply asleep" a dormant pattern is - measuring the energy required to activate it and the stability of its dormant state. Deeper dormancy implies greater resistance to activation but potentially larger effects when awakened.

Usage

dormancy_depth(
  dormancy_result,
  method = "combined",
  normalize = TRUE,
  verbose = FALSE
)

Arguments

dormancy_result	An object of class "dormancy" from dormancy_detect.
method	Character. The depth measurement method: "energy" - Measures the statistical "energy barrier" to activation "stability" - Measures how stable the dormant state is "entropy" - Measures information-theoretic dormancy depth "combined" - Weighted combination of all methods Default is "combined".
normalize	Logical. Whether to normalize depth scores to [0, 1]. Default is TRUE.
verbose	Logical. Whether to print progress messages. Default is FALSE.

Details

Dormancy depth is a novel concept in statistical analysis, inspired by:

• Physics: Potential energy barriers in phase transitions

• Biology: Depth of seed dormancy (stratification requirements)

• Geology: Locked fault segments and earthquake potential

A deeply dormant pattern:

• Requires significant change in conditions to activate

• Is stable against minor perturbations

• May have a larger effect when finally awakened

• Represents accumulated "potential energy" in the system

The depth measurement helps prioritize which patterns to monitor and what magnitude of change would be required to awaken them.

Value

A list containing:

• depths - Data frame with depth measurements for each pattern

• depth_distribution - Summary statistics of depth distribution

• awakening_effort - Estimated effort required to activate each pattern

• stability_index - Stability measure for each pattern's dormant state

Examples

set.seed(42)
n <- 500
x <- rnorm(n)
# Create a deeply dormant pattern (only active in extreme conditions)
z <- ifelse(abs(x) > 2, 1, 0)
y <- ifelse(z == 1, 0.9 * x + rnorm(sum(z), 0, 0.1), rnorm(n))
data <- data.frame(x = x, y = y)

result <- dormancy_detect(data, method = "threshold")
depths <- dormancy_depth(result)
print(depths)

dormancy documentation built on March 16, 2026, 5:09 p.m.