Generates a synthetic one-row data frame of cascade survey parameters,
suitable for passing directly to analyze_cascade(). The parameters
represent a plausible set of cascade network inputs: Layer 1 team size
(split into two types), Layer 2 reach per type, Layer 3 reach, and
probabilistic cross-connection rates for each layer.
Usage
generate_cascade_data(seed = Sys.time())Arguments
- seed
Integer or POSIXct. The seed for random number generation. Defaults to
Sys.time().
Value
A one-row data frame with columns matching the cascade_d*
survey schema consumed by analyze_cascade().
Details
The returned data frame has the same column schema as the cascade
sub-frame produced by load_survey_data(), so it can be used as a
drop-in replacement for testing and examples without needing a real survey
file.
Parameter ranges:
cascade_d1_people_1_1: Layer 1 Type 1 count (2–6).cascade_d1_people_2_1: Layer 1 Type 2 count (2–6).cascade_d2_people_1_1: Layer 2 children per Type 1 parent (1–4).cascade_d2_people_2_1: Layer 2 children per Type 2 parent (1–4).cascade_d2_stats_1: L2-L2 cross-connection probability (0.05–0.40).cascade_d2_stats_2: L2->L1 back-edge probability (0.05–0.30).cascade_d3_people: Layer 3 children per Layer 2 parent (1–4).cascade_d3_stats_1: L3-L3 cross-connection probability (0.02–0.20).cascade_d3_stats_2: L3->L2 back-edge probability (0.02–0.20).
References
Price, J. F. (2024). CEnTR*IMPACT: Community Engaged and Transformative Research – Inclusive Measurement of Projects & Community Transformation (CUMU-Collaboratory Fellowship Report). Coalition of Urban and Metropolitan Universities.
Examples
params <- generate_cascade_data(seed = 42)
result <- analyze_cascade(params)
#> Running full exact analysis (~68 expected edges).
print(result$cascade_score)
#> [1] 0.768217