dormancy_detect: Detect Dormant Patterns in Multivariate Data

In dormancy: Detection and Analysis of Dormant Patterns in Data

Detect Dormant Patterns in Multivariate Data

Description

Identifies dormant patterns in multivariate data - statistical relationships that exist but are currently inactive. Dormant patterns only manifest when specific trigger conditions emerge in the data.

Usage

dormancy_detect(
  data,
  threshold = 0.3,
  method = "conditional",
  n_bins = 10,
  min_cluster = 0.05,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

data	A numeric matrix or data frame with observations in rows and variables in columns.
threshold	Numeric. The minimum dormancy score for a pattern to be considered significant. Default is 0.3.
method	Character. The detection method to use. Options are: "conditional" - Detects patterns that are conditionally suppressed (active only under specific conditions) "threshold" - Detects patterns that emerge when variables cross certain thresholds "phase" - Detects patterns that exist in specific phase regions of the data space "cascade" - Detects cascade-ready patterns that could trigger chain reactions Default is "conditional".
n_bins	Integer. Number of bins for discretizing continuous variables when searching for conditional patterns. Default is 10.
min_cluster	Numeric. Minimum proportion of observations that must be in a region for it to be considered. Default is 0.05 (5%).
parallel	Logical. Whether to use parallel processing. Default is FALSE.
verbose	Logical. Whether to print progress messages. Default is FALSE.

Details

Dormant patterns are fundamentally different from weak or spurious correlations. A dormant pattern is a genuine relationship that is currently suppressed by prevailing conditions in the data. The key insight is that:

1. The pattern exists (verified through subset analysis)

2. The pattern is currently inactive (not visible in aggregate statistics)

3. The pattern can "awaken" when conditions change

The detection algorithm works by:

1. Segmenting the data space into regions

2. Computing pairwise relationships in each region

3. Identifying relationships that vary significantly across regions

4. Flagging relationships that are strong in some regions but weak overall

5. Estimating the conditions under which dormant patterns would activate

Value

A list of class "dormancy" containing:

• patterns - A data frame of detected dormant patterns with columns: variables, dormancy_score, trigger_variable, trigger_region, activation_risk

• data - The input data

• method - The detection method used

• threshold - The threshold used

• conditional_regions - List of regions where patterns are dormant

• metadata - Additional information about the detection process

See Also

dormancy_trigger for identifying activation triggers, dormancy_depth for measuring dormancy depth, awaken for simulating pattern activation

Examples

# Create data with a dormant pattern
set.seed(42)
n <- 1000
x <- rnorm(n)
z <- sample(c(0, 1), n, replace = TRUE)
# Relationship between x and y only exists when z == 1
y <- ifelse(z == 1, 0.8 * x + rnorm(n, 0, 0.3), rnorm(n))
data <- data.frame(x = x, y = y, z = factor(z))

# Detect dormant patterns
result <- dormancy_detect(data, method = "conditional")
print(result)

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