Simulates what would happen if a dormant pattern were to "awaken" - i.e., become active. This function allows exploration of potential future states and scenario analysis without waiting for actual pattern activation.
Usage
awaken(
dormancy_result,
pattern_id = 1,
intensity = 1,
n_sim = 100,
return_data = FALSE,
verbose = FALSE
)Arguments
- dormancy_result
An object of class "dormancy" from
dormancy_detect.- pattern_id
Integer or "all". Which pattern(s) to awaken. Default is 1.
- intensity
Numeric. Intensity of awakening, from 0 (dormant) to 1 (fully active). Default is 1.
- n_sim
Integer. Number of simulation runs. Default is 100.
- return_data
Logical. Whether to return simulated data. Default is FALSE.
- verbose
Logical. Whether to print progress messages. Default is FALSE.
Value
A list containing:
awakening_effects- Data frame describing the effects of awakeningsimulated_stats- Summary statistics from simulationscascade_effects- Effects on other patterns (if any)simulated_data- Ifreturn_data = TRUE, simulated datasets
Details
Awakening simulation is valuable for:
Scenario planning and stress testing
Understanding potential system behaviors
Preparing for pattern activation events
Testing the robustness of current strategies
The simulation works by:
Identifying the dormant pattern's trigger conditions
Simulating data where those conditions are met
Applying the pattern's relationship to the simulated data
Measuring the resulting effects on the system
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")
awakening <- awaken(result, pattern_id = 1, n_sim = 50)
#> Warning: No dormant patterns to awaken.
print(awakening)
#> $awakening_effects
#> data frame with 0 columns and 0 rows
#>
#> $simulated_stats
#> NULL
#>
#> $cascade_effects
#> NULL
#>