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Assess Risk of Dormant Pattern Activation

Source: R/dormancy_risk.R
dormancy_risk.Rd

Quantifies the risk associated with dormant patterns, including the probability of activation, potential impact, and uncertainty in risk estimates. This function provides actionable risk metrics for decision-making and monitoring priorities.

Usage

dormancy_risk(
  dormancy_result,
  depth_result = NULL,
  impact_weights = NULL,
  time_horizon = 1,
  risk_tolerance = 0.3,
  verbose = FALSE
)

Arguments

dormancy_result

An object of class "dormancy" from dormancy_detect.

depth_result

Optional. An object of class "dormancy_depth" from dormancy_depth. If provided, uses depth information for more accurate risk assessment.

impact_weights

Optional named vector of weights for different impact types. Default considers symmetric positive/negative impacts.

time_horizon

Numeric. The time horizon for risk assessment (in abstract units). Longer horizons increase activation probability. Default is 1.

risk_tolerance

Numeric. Risk tolerance threshold for flagging. Default is 0.3.

verbose

Logical. Whether to print progress messages. Default is FALSE.

Value

A list containing:

  • risk_scores - Data frame with risk metrics for each pattern

  • risk_matrix - Matrix of activation probability x impact

  • priorities - Ordered list of patterns by risk priority

  • recommendations - Risk management recommendations

  • summary - Overall risk summary statistics

Details

Risk assessment for dormant patterns considers multiple dimensions:

  • Activation Probability: Likelihood that trigger conditions will be met in the given time horizon

  • Impact Magnitude: Expected effect size if the pattern activates

  • Impact Direction: Whether activation would be beneficial, harmful, or neutral

  • Cascade Potential: Risk of triggering other patterns

  • Uncertainty: Confidence in risk estimates

The risk score combines these dimensions into an actionable metric: $$Risk = P(activation) \times Impact \times CascadeFactor \times (1 + Uncertainty)$$

Examples

set.seed(42)
n <- 500
x <- rnorm(n)
z <- sample(c(0, 1), n, replace = TRUE)
y <- ifelse(z == 1, 0.8 * x + rnorm(sum(z), 0, 0.3), rnorm(n))
#> Warning: longer object length is not a multiple of shorter object length
data <- data.frame(x = x, y = y, z = factor(z))

result <- dormancy_detect(data, method = "conditional")
risk <- dormancy_risk(result, time_horizon = 2)
#> Warning: No dormant patterns to assess risk for.
print(risk)
#> $risk_scores
#> data frame with 0 columns and 0 rows
#> 
#> $risk_matrix
#> NULL
#> 
#> $priorities
#> character(0)
#> 
#> $recommendations
#> [1] "No dormant patterns detected."
#> 
#> $summary
#> NULL
#>