plot_stacked_comp: Stacked composition plot

In pbs-assess/stockseasonr: Seasonal Predictions of Stock Composition and Abundance

Stacked composition plot

Description

Stacked composition plot

Usage

plot_stacked_comp(
  x_var,
  grouping_var,
  facet_var = NULL,
  ssdr,
  comp_dat,
  pred_dat
)

Arguments

x_var	Explanatory variable within pred_dat to be plotted on x-axis (must be continuous).
grouping_var	Character or factor variable within pred_dat that was used to generate composition estimates.
facet_var	Optional character or factor variable within pred_dat corresponding to categorical main effects in fitted model.
ssdr	Parameter estimates generated by stockseasonr::fit_stockseasonr()$ssdr.
comp_dat	Composition dataframe; each row represents a sampling event (e.g. genetic samples collected from a given day and region)
pred_dat	Dataframe of fixed effects composition predictions

Value

Stacked ribbon plot representing trends in mean composition with optional facets.

Examples

dum_pred <- expand.grid(
  month_n = seq(1, 12, by = 0.1),
  region = unique(comp_ex$region)
)
# model fitting will take several seconds
m1 <- fit_stockseasonr(
  comp_formula = agg ~ 1 + region + 
    s(month_n, bs = "tp", k = 4, m = 2) + 
    (1 | year),
  comp_dat = comp_ex,
  pred_dat = dum_pred,
  model = "dirichlet",
  random_walk = TRUE,
  fit = TRUE,
  nlminb_loops = 2, newton_loops = 1
)
plot_stacked_comp(x_var = "month_n", grouping_var = "agg", 
                  facet_var = "region", ssdr = m1$ssdr, comp_dat = comp_ex,
                  pred_dat = dum_pred)

pbs-assess/stockseasonr documentation built on April 25, 2024, 12:15 p.m.