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Measure the Depth of Dormancy in Patterns

Source: R/dormancy_depth.R
dormancy_depth.Rd

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.

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

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.

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))
#> Warning: longer object length is not a multiple of shorter object length
data <- data.frame(x = x, y = y)

result <- dormancy_detect(data, method = "threshold")
depths <- dormancy_depth(result)
#> Warning: No dormant patterns to measure depth for.
print(depths)
#> $depths
#> data frame with 0 columns and 0 rows
#> 
#> $depth_distribution
#> NULL
#> 
#> $awakening_effort
#> NULL
#> 
#> $stability_index
#> NULL
#>