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Detect Dormant Patterns in Multivariate Data

Source: R/dormancy_detect.R
dormancy_detect.Rd

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.

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

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

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 Detection Results
#> ==========================
#> 
#> Method: conditional 
#> Threshold: 0.3 
#> Observations: 1000 
#> Variables: 2 
#> 
#> Patterns detected: 0