Methods for Creating, Accessing and Assigning Timeseries objects

An object of class Timeseries contains information for the Time Series chart. The Timeseries function is used both to create and modify an Timeseries-class() object, and to access and assign Timeseries for an object of class Slick-class(). See Details.

Usage

Timeseries(
  Code = "",
  Label = "",
  Description = "",
  Time = numeric(),
  TimeNow = numeric(),
  TimeLab = "Year",
  Value = array(),
  Preset = list(),
  Target = NULL,
  Limit = NULL
)

Timeseries(Slick) <- value

# S4 method for class 'missing'
Timeseries()

# S4 method for class 'character'
Timeseries(
  Code = "",
  Label = "",
  Description = "",
  Time = numeric(),
  TimeNow = numeric(),
  TimeLab = "Year",
  Value = array(),
  Preset = list(),
  Target = NULL,
  Limit = NULL
)

# S4 method for class 'list'
Timeseries(
  Code = "",
  Label = "",
  Description = "",
  Time = numeric(),
  TimeNow = numeric(),
  TimeLab = "Year",
  Value = array(),
  Preset = list(),
  Target = NULL,
  Limit = NULL
)

# S4 method for class 'Slick'
Timeseries(Code)

# S4 method for class 'Slick'
Timeseries(Slick) <- value

Arguments

Code: A short code for the Performance Indicators for this object. A character string length nPI or a named list for multi-language support. See Details
Label: A short label for the Performance Indicators for this object. Used to label axes on charts. Can be longer than Code but recommended to keep short as possible so it shows clearly in plots and tables. A character string length nPI or a named list for multi-language support. See Details
Description: A description for the Performance Indicators for this object. Can include Markdown, see Examples. A character string length nPI or a named list for multi-language support. See Details
Time: A numeric vector with values for the historical and projection time-steps. Must match length nTS in Value
TimeNow: A numeric value matching the last historical timestep in Time
TimeLab: Character string length 1. Name of the time step (e.g., 'Year'). Will be used as the label in the Timeseries plot. Use a named list for multiple languages.
Value: A numeric array with the stochastic performance indicator values for each simulation (sim), operating model (OM), management procedure (MP), performance indicator (PI), and historical + projection timestep (nTS). Dimensions: c(nsim, nOM, nMP, nPI, nTS)
Preset: An optional named list for the preset buttons in the App(). The name of the list element will appear as a button in the App().
Target: Numeric vector length nPI with the target value for the PIs.
Limit: Numeric vector length nPI with the limit value for the PIs.
Slick: A Slick-class() object
value: A Timeseries-class() object

Details

Use plotTimeseries() to create the boxplot from the console.

Note

Character strings in Code, Label, and Description must all be same length as the number of performance indicators (nPIs) in Value

Objects of class Timeseries are created with Timeseries()

Multi-Language Support

Text with multi-language supported can be provided as a named list. Available languages:

en: English (default)
es: Spanish
fr: French

Use the Code(), Label(), Description(), Value(), Preset() functions to access and assign the values for an existing Timeseries object, see Examples

Functions

Timeseries(missing): Create an empty Timeseries object
Timeseries(character): Create a populated Timeseries object
Timeseries(list): Create a populated Timeseries object
Timeseries(Slick): Return Timeseries from a Slick-class() object
Timeseries(Slick) <- value: Assign a Timeseries-class() object to a Slick-class() object

Examples

# Generate dummy values
nsim <- 10
nOM <- 2
nMP <- 4
nPI <- 3
nHistTS <- 50
nProjTS <- 30
nTS <- nHistTS + nProjTS

values <- array(NA, dim=c(nsim, nOM, nMP, nPI, nTS))

pi_means <- c(1,1, 1000)

for (om in 1:nOM) {
  for (pi in 1:nPI) {
    # PI identical for historical
    histVals <- matrix(
      pi_means[pi] *
        cumprod(c(rlnorm(nHistTS*nsim, 0, 0.05))),
      nrow=nsim, ncol=nHistTS, byrow=TRUE)
    histVals <- replicate(nMP, histVals)
    values[,om, , pi,1:nHistTS] <- aperm(histVals, c(1,3,2))

    for (mp in 1:nMP) {
      values[,om, mp, pi,(nHistTS+1):nTS] <- matrix(
        pi_means[pi] *
          cumprod(c(rlnorm(nProjTS*nsim, 0, 0.05))),
        nrow=nsim, ncol=nProjTS, byrow=TRUE)
    }
  }
}

# Create and populate Object
timeseries <- Timeseries(Code=c('B/BMSY', 'F/FMSY', 'TAC'),
                         Label=c('B/BMSY',
                                 'F/FMSY',
                                 'TAC'),
                         Description = c('This is the description for PI 1',
                                         'This is the description for PI 2',
                                         'This is the description for PI 3'),
                         Value=values
)

# Last historical time step
TimeNow(timeseries) <- 2024

# Add values for time steps
Time(timeseries) <- c(seq(TimeNow(timeseries), by=-1, length.out=nHistTS),
                      seq(TimeNow(timeseries)+1, by=1, length.out=nProjTS))

# Check
Check(timeseries)
#> 
#> ── Checking: "Timeseries" ──
#> 
#> ✔ Complete

# Add to `Slick` object
slick <- Slick()
Timeseries(slick) <- timeseries

# Plots
plotTimeseries(slick, 3)
#> ℹ Note: `MPs` is empty. Using default MP names and colors


plotTimeseries(slick, byMP=TRUE)
#> ℹ Note: `MPs` is empty. Using default MP names and colors


plotTimeseries(slick, byOM=TRUE)
#> ℹ Note: `MPs` is empty. Using default MP names and colors

Methods for Creating, Accessing and Assigning `Timeseries` objects