plot.biomass: Compare the estimated biomass across different models.

In PacificCommunity/ofp-sam-diags4MFCL: R Functions for Presenting MULTIFAN-CL Results and Diagnostics

Compare the estimated biomass across different models.

Description

Compare the estimated biomass across different models.

Usage

plot.biomass(
  rep.list,
  rep.names = NULL,
  agg.years = TRUE,
  agg.regions = TRUE,
  biomass.type = "SSB",
  biomass.units = 1000,
  yaxis.free = FALSE,
  palette.func = default.model.colours,
  save.dir,
  save.name,
  ...
)

Arguments

rep.list	A list of MFCLRep objects or a single MFCLRep object. The reference model should be listed first.
rep.names	A vector of character strings naming the models for plotting purposes. If not supplied, model names will be taken from the names in the rep.list (if available) or generated automatically.
agg.years	TRUE or FALSE. Should model outputs be aggregated to an annual time step.
agg.regions	TRUE or FALSE. Should model outputs be aggregated across all regions are kept separate.
biomass.type	Character string denoting the type of biomass plotted, 'SSB' or 'Total'
biomass.units	Integer number denoting how many MT to divide the biomass by. Default is 1000.
yaxis.free	TRUE or FALSE. Default is FALSE. If TRUE and agg.regions is also TRUE than the y-axis scales will be independent across regions, otherwise they will be shared so regional scaling will be apparent.
palette.func	A function to determine the colours of the models. The default palette has the reference model in black. It is possible to determine your own palette function. Two functions currently exist: default.model.colours() and colourblind.model.colours().
save.dir	Path to the directory where the outputs will be saved
save.name	Name stem for the output, useful when saving many model outputs in the same directory
...	Passes extra arguments to the palette function. Use the argument all.model.colours to ensure consistency of model colours when plotting a subset of models.

PacificCommunity/ofp-sam-diags4MFCL documentation built on Oct. 11, 2023, 1:32 a.m.