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Create relative sensitivity plots as described in Cope and Gertseva (2020)

Source: R/SS_Sensi_plot.R
SS_Sensi_plot.Rd

Uses output from SSsummarize() to make a figure showing sensitivity of various quantities of interest.

Usage

SS_Sensi_plot(
  model.summaries,
  dir = "",
  current.year,
  mod.names,
  Sensi.RE.out = "Sensi_RE_out.DMP",
  CI = 0.95,
  TRP.in = 0.4,
  LRP.in = 0.25,
  sensi_xlab = "Sensitivity scenarios",
  ylims.in = c(-1, 2, -1, 2, -1, 2, -1, 2, -1, 2, -1, 2),
  plot.figs = c(1, 1, 1, 1, 1, 1),
  sensi.type.breaks = NA,
  anno.x = NA,
  anno.y = NA,
  anno.lab = NA,
  spawn.lab = NA,
  yield.lab = NA,
  F.lab = NA
)

Arguments

model.summaries

Output from SSsummarize() summarizing results of models to be included

dir

A file path to the directory of interest. The default value is dir = NULL, which leads to using the current working directory.

current.year

Year to report output

mod.names

List the names of the sensitivity runs

Sensi.RE.out

Saved file of relative changes

CI

Confidence interval box based on the reference model

TRP.in

Target relative abundance value

LRP.in

Limit relative abundance value

sensi_xlab

X-axis label

ylims.in

Y-axis label

plot.figs

Which plots to make/save?

sensi.type.breaks

vertical breaks that can separate out types of sensitivities

anno.x

Horizontal positioning of the sensitivity types labels

anno.y

Vertical positioning of the sensitivity types labels

anno.lab

Sensitivity types labels

spawn.lab

Label for spawning output or spawning biomass. By default it will be set to "SO" if any model has spawning output in numbers and "SB" if all models have spawning output in biomass. Subscripts will be added for 0 or current year.

yield.lab

Label for yield reference point. By default it will be set to something like "Yield(SPR=0.3)" where the SPR value is the SPR target. If the models have different SPR targets, it will be set to "Yield(tgt SPR)".

F.lab

Label for F reference point. By default it will be set to something like "F(SPR=0.3)" where the SPR value is the SPR target. If the models have different SPR targets, it will be set to "F(tgt SPR)".

References

Cope, J. and Gertseva, V. 2020. A new way to visualize and report structural and data uncertainty in stock assessments. Can. J. Fish. Aquat. Sci. 77:1275-1280. https://doi.org/10.1139/cjfas-2020-0082

See also

SSsummarize()

Author

Jason Cope

Examples

if (FALSE) { # \dontrun{
# Set directory and extract ouput from models
# Model 1 needs to be the Reference model, with sensitivity runs following
# from run 2 on.

# Note: models are available in Jason Cope's github repository:
# https://github.com/shcaba/Stock-Assessment-Sensitivity-Plots/
dir <-
  "C:/Users/.../GitHub/Stock-Assessment-Sensitivity-Plots/Sensitivity_runs/"
models.dirs <- paste0("Cab_SCS_MS_", 1:19)
zz <- SSgetoutput(dirvec = file.path(dir, models.dirs))

# Use the summarize function in r4ss to get model summaries
model.summaries <- SSsummarize(zz)

# Define the names of each model. This will be used to label runs in the
# table and in the figures.
mod.names <- c(
  "Reference",
  "M: Fix to 2009",
  "M: Fix to prior",
  "M: Fix to Hamel",
  "M: Fix to VBGF",
  "M: Fix to OR",
  "VBGF 2009",
  "VBGF Grebel",
  "OR maturity",
  "Est. h",
  "All rec devs",
  "No rec devs",
  "High bias adj.",
  "Harmonic mean",
  "Dirichlet",
  "Wts = 1",
  "No blocks",
  "First blocks in 2000",
  "Alt rec catches"
)

# Run the sensitivity plot function
SS_Sensi_plot(
  model.summaries = model.summaries,
  dir = dir,
  current.year = 2019,
  mod.names = mod.names, # List the names of the sensitivity runs
  likelihood.out = c(1, 1, 0),
  Sensi.RE.out = "Sensi_RE_out.DMP", # Saved file of relative errors
  CI = 0.95, # Confidence interval box based on the reference model
  TRP.in = 0.4, # Target relative abundance value
  LRP.in = 0.25, # Limit relative abundance value
  sensi_xlab = "Sensitivity scenarios", # X-axis label
  ylims.in = c(-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1), # Y-axis label
  plot.figs = c(1, 1, 1, 1, 1, 1), # Which plots to make/save?
  sensi.type.breaks = c(6.5, 9.5, 13.5, 16.5), # vertical breaks
  anno.x = c(3.75, 8, 11.5, 15, 18), # positioning of types labels
  anno.y = c(1, 1, 1, 1, 1), # positioning of types labels
  anno.lab = c(
    "Natural mortality", "VBGF/Mat.", "Recruitment", "Data Wts.",
    "Other"
  ) # Sensitivity types labels
)
} # }