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Sweep one design parameter and record performance metrics

Source: R/analysis-sweep.R
rh_sweep.Rd

Runs rh_simulate() repeatedly while varying a single parameter, keeping the others fixed, and collects one or more summary metrics. Useful to see how the system responds to a single "lever" (e.g. reliability versus reservoir capacity). Returns a plain data frame, so it works without ggplot2.

Usage

rh_sweep(
  precip,
  base = list(),
  param,
  values,
  metrics = "attendance_pct",
  dates = NULL,
  climatology = FALSE
)

Arguments

precip

Numeric vector of daily precipitation (mm). If climatology = TRUE, it is first collapsed to its day-of-year mean (see dates).

base

A named list of the fixed arguments passed to rh_simulate() (e.g. list(demand = 6.6, area = 4170, capacity = 400, runoff = 0.85, efficiency = 1)). Must supply every required argument except the one being swept.

param

Name of the parameter to vary: one of "demand", "area", "capacity", "runoff", "efficiency", "initial".

values

Numeric vector of values for param.

metrics

Character vector of metric names to record (any of the columns of rh_metrics()); default "attendance_pct".

dates

Optional date vector aligned with precip, required only when climatology = TRUE.

climatology

If TRUE, simulate on the day-of-year climatology (much faster) instead of the full series. Default FALSE.

Value

A data frame with one column named after param (the swept values) plus one column per requested metric.

See also

rh_grid() for two parameters at once.

Examples

caps <- seq(50, 600, by = 50)
rh_sweep(precip_pi$value, base = list(demand = 6.6, area = 4170),
         param = "capacity", values = caps,
         metrics = c("attendance_pct", "reliability_pct"))
#>    capacity attendance_pct reliability_pct
#> 1        50       41.25324        38.67393
#> 2       100       49.20007        47.47391
#> 3       150       53.37051        51.82870
#> 4       200       56.35354        55.00827
#> 5       250       58.67158        57.45687
#> 6       300       60.83589        59.74274
#> 7       350       62.88305        61.87364
#> 8       400       64.85176        63.92706
#> 9       450       66.70168        65.82292
#> 10      500       68.47761        67.62837
#> 11      550       70.18857        69.36925
#> 12      600       71.79939        71.00682