These functions are simple plotting helpers to get some quick visuals of values produced by sim_abundance, sim_distribution, etc.

plot_trend(sim, sum_ages = sim$ages, col = viridis::viridis(1), ...)

plot_surface(sim, mat = "N", xlab = "Age", ylab = "Year", zlab = mat, ...)

plot_grid(grid, ...)

plot_distribution(
  sim,
  ages = sim$ages,
  years = sim$years,
  type = "contour",
  scale = "natural",
  ...
)

plot_survey(sim, which_year = 1, which_sim = 1)

plot_total_strat_fan(sim, surveys = 1:5, quants = seq(90, 10, by = -10), ...)

plot_length_strat_fan(
  sim,
  surveys = 1:5,
  years = 1:10,
  lengths = 1:50,
  select_by = "year",
  quants = seq(90, 10, by = -10),
  ...
)

plot_age_strat_fan(
  sim,
  surveys = 1:5,
  years = 1:10,
  ages = 1:10,
  select_by = "year",
  quants = seq(90, 10, by = -10),
  ...
)

plot_error_surface(sim, plot_by = "rule")

plot_survey_rank(sim, which_strat = "age")

Arguments

sim

Object returned by sim_abundance, sim_distribution, etc.

sum_ages

Sum across these ages

col

Plot color

...

Additional arguments to pass to plot_ly.

mat

Name of matrix in sim list to plot.

xlab, ylab, zlab

Axes labels.

grid

Grid produced by make_grid.

ages

Subset data to one or more ages.

years

Subset data to one or more years.

type

Plot type: "contour" or "heatmap".

scale

Plot response on "natural" or "log" scale?

which_year

Subset to specific year

which_sim

Subset to specific sim

surveys

Subset data to one or more surveys.

quants

Quantile intervals to display on fan plot

lengths

Subset data to one or more length groups.

select_by

Select plot by "age", "length" or "year"?

plot_by

Plot error surface by "rule" or "samples"?

which_strat

Which strat values to focus on? (total, length, or age)

Value

Returns a plot of class plotly.