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S4 class Timeseries

Source: R/class_Timeseries.R
Timeseries-class.Rd

Objects of class Timeseries are used to store information for the Time Series plots. Like all S4 objects in Slick, slots in this object can be accessed and assigned using functions corresponding to slot name. See Timeseries() and the the See Also section below.

Details

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

Note

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

Slots

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. Can also be class Date, POSIXct or POSIXt

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 plots. 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.

RefPoints

List for setting custom Reference Points. Overrides Target and Limit. See Details section in Timeseries().

Misc

A named list for additional miscellaneous information.

See also

Timeseries(), Code(), Label(), Description(), Value(), Preset()

Examples

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

set.seed(101)

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=FALSE)
    }
  }
}

# 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(rev(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)
#> ℹ Note: `MPs` is empty. Using default MP names and colors

plotTimeseries(slick, 2)
#> ℹ Note: `MPs` is empty. Using default MP names and colors

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


# Custom Reference Points
RefPoints(timeseries) <- list(
  list(Name=c('0.5 BMSY', 'BMSY', '1.5 BMSY'),
       Value=c(0.5, 1, 1.5),
       Color=c('red', 'orange', 'green')),
  list(Name=c('0.8 FMSY', 'FMSY'),
       Value=c(0.8,1),
       Color=c('orange', 'red')),
  list(Name='Target Catch',
       Value=1200,
       Color='blue')
)

Timeseries(slick) <- timeseries
plotTimeseries(slick)
#> ℹ Note: `MPs` is empty. Using default MP names and colors

plotTimeseries(slick, 2)
#> ℹ Note: `MPs` is empty. Using default MP names and colors

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