plot_biomass: Plot biomass from MCMC output for one or more models....

In pacific-hake/hake-assessment: Stock Assessment for Pacific Hake

Plot biomass from MCMC output for one or more models. Includes the virgin biomass, B0

Description

Plot biomass from MCMC output for one or more models. Includes the virgin biomass, B0

Create a plot of cohorts by age as a set of lines

Plot the relative spawning biomass with several forecast trajectories

Create a plot of exploitation fraction

Create a plot of fishing intensity

Create a plot of forecast value comparisons with the catch level on the x-axis and probability as the y axis value

Plot recruitment deviations from MCMC output for one or more models

Plot recruitment from MCMC output for one or more models

Plot relative biomass from MCMC output for one or more models

Plot recruitment from MCMC output for one or more models, relative to a particular year

Plot survey index fits from MCMC output for one or more models, for one of the acoustic surveys

Usage

plot_biomass(
  model_lst = NULL,
  model_names,
  d_obj = NULL,
  show_arrows = TRUE,
  xlim = c(1964, year(Sys.time())),
  x_breaks = 1966:year(Sys.time()),
  x_labs_mod = 5,
  x_expansion = 3,
  tick_prop = 1,
  vjust_x_labels = -0.25,
  ylim = c(0, 4.5),
  y_breaks = seq(ylim[1], ylim[2], by = 0.5),
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  dodge_bo = 3,
  rev_colors = TRUE,
  wrap_y_label = FALSE,
  alpha = ts_ribbon_alpha,
  point_size = ifelse(is_single_model, ts_single_model_pointsize, ts_pointsize),
  point_color = ts_single_model_pointcolor,
  point_shape = ifelse(is_single_model, ts_single_model_pointshape, ts_pointshape),
  point_stroke = ifelse(is_single_model, ts_single_model_pointstroke, ts_pointstroke),
  line_width = ifelse(is_single_model, ts_single_model_linewidth, ts_linewidth),
  line_gap = ts_linegap,
  single_ribbon_lines_color = ts_single_model_linecolor,
  single_ribbon_fill = ts_single_model_ribbon_fill,
  ribbon_line_type = ifelse(is_single_model, ts_single_model_ribbon_linetype,
    ts_ribbon_linetype),
  refpt_bo_linecolor = refpt_bo_linecolor,
  refpt_usr_linecolor = refpt_usr_linecolor,
  refpt_lrp_linecolor = refpt_lrp_linecolor,
  refpt_bo_linewidth = refpt_bo_linewidth,
  refpt_usr_linewidth = refpt_usr_linewidth,
  refpt_lrp_linewidth = refpt_lrp_linewidth,
  refpt_bo_linetype = refpt_bo_linetype,
  refpt_usr_linetype = refpt_usr_linetype,
  refpt_lrp_linetype = refpt_lrp_linetype
)

plot_cohort_catch(
  model,
  cohorts = c(1999, 2010, 2014, 2016, 2020),
  ages = 0:20,
  y_breaks = seq(0, 1250, 250),
  leg_pos = c(0.85, 0.5),
  leg_ncol = 1,
  leg_font_size = 12,
  axis_title_font_size = 14,
  axis_tick_font_size = 11,
  line_width = 1,
  show_arrowheads = TRUE,
  arrow_size = 0.03
)

plot_depl_fore_comparison(
  model,
  fore_inds = c(1, 2, model$ct_levels_vals$ct_actual_ind,
    model$ct_levels_vals$ct_tac_ind, model$ct_levels_vals$ct_default_policy_ind),
  xlim = c(max(forecast_yrs) - 16, max(forecast_yrs) - 1),
  x_breaks = xlim[1]:xlim[length(xlim)],
  x_labs_mod = 5,
  show_arrows = FALSE,
  x_expansion = 1,
  tick_prop = 1,
  vjust_x_labels = -2,
  ylim = c(0, 2),
  y_breaks = c(0, 0.1, 0.4, 0.5, 1, 1.5, 2),
  y_labels = expression("0", "0.1B"[0], "0.4B"[0], "0.5", "B"[0], "1.5", "2"),
  y_colors = c("black", "red", "green", "black", "blue", "black", "black"),
  alpha = 0.2,
  leg_pos = c(0.15, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  forecast_yrs,
  point_size = ts_pointsize,
  point_shape = ts_pointshape
)

plot_exploitation_fraction(
  model,
  show_arrows = TRUE,
  xlim = c(1964, year(Sys.time()) - 1),
  x_breaks = 1966:year(Sys.time()),
  x_labs_mod = 5,
  x_expansion = 1,
  tick_prop = 1,
  ylim = c(0, 0.25),
  y_breaks = seq(ylim[1], ylim[2], by = 0.05),
  point_size = ts_single_model_pointsize,
  point_color = ts_single_model_pointcolor,
  point_shape = ts_single_model_pointshape,
  point_stroke = ts_single_model_pointstroke,
  line_width = ts_single_model_linewidth,
  line_color = ts_single_line_color
)

plot_fishing_intensity(
  model,
  show_arrows = TRUE,
  xlim = c(1964, year(Sys.time()) - 1),
  x_breaks = 1966:year(Sys.time()),
  x_labs_mod = 5,
  x_expansion = 1,
  tick_prop = 1,
  ylim = c(0, 1.4),
  y_breaks = seq(ylim[1], ylim[2], by = 0.2),
  point_size = ts_single_model_pointsize,
  point_color = ts_single_model_pointcolor,
  point_shape = ts_single_model_pointshape,
  point_stroke = ts_single_model_pointstroke,
  line_width = ts_single_model_linewidth,
  line_color = ts_single_line_color,
  fspr40_text_x = xlim[1] + 2,
  fspr40_text_y = 1.05
)

plot_fore_compare(
  model,
  forecast_yrs,
  fore_yr,
  colors = c("black", "blue", "green", "orange", "red", "tan"),
  shapes = c(16, 17, 17, 17, 15, 18),
  x_expansion = 40,
  leg_font_size = 8,
  leg_pos = NULL,
  leg_ncol = 1,
  remove_x_val = NULL,
  show_50_line = TRUE,
  short = FALSE
)

plot_recdevs(
  model_lst = NULL,
  model_names = NULL,
  d_obj = NULL,
  show_arrows = TRUE,
  xlim = c(1946, year(Sys.time())),
  x_breaks = xlim[1]:xlim[2],
  x_labs_mod = 5,
  x_expansion = 2,
  tick_prop = 1,
  vjust_x_labels = -1,
  ylim = c(-5, 5),
  y_breaks = seq(ylim[1], ylim[2], by = 1),
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  alpha = 1,
  point_color = ts_single_model_pointcolor,
  point_size = ifelse(is_single_model, ts_single_model_pointsize, ts_pointsize),
  point_shape = ifelse(is_single_model, ts_single_model_pointshape, ts_pointshape),
  point_stroke = ifelse(is_single_model, ts_single_model_pointstroke, ts_pointstroke),
  line_width = ifelse(is_single_model, ts_single_model_linewidth, ts_linewidth),
  line_type = ts_single_model_linetype,
  line_color = ts_single_model_linecolor,
  dodge_val = 1,
  rev_colors = FALSE,
  ...
)

plot_recruitment(
  model_lst = NULL,
  model_names = NULL,
  show_arrows = TRUE,
  inc_means = FALSE,
  relative = FALSE,
  xlim = c(1966, year(Sys.time())),
  x_breaks = xlim[1]:xlim[2],
  x_labs_mod = 5,
  x_expansion = 2,
  tick_prop = 1,
  vjust_x_labels = -1,
  ylim = c(0, 40),
  y_breaks = seq(ylim[1], ylim[2], by = 10),
  y_labels = y_breaks,
  y_colors = rep("black", length(y_breaks)),
  alpha = 0.3,
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  point_size = ifelse(is_single_model, ts_single_model_pointsize, ts_pointsize),
  color = ts_single_model_pointcolor,
  point_shape = ifelse(is_single_model, ts_single_model_pointshape, ts_pointshape),
  point_stroke = ifelse(is_single_model, ts_single_model_pointstroke, ts_pointstroke),
  line_width = ifelse(is_single_model, ts_single_model_linewidth, ts_linewidth),
  horizontal_line_color = "darkgreen",
  crossbar_width = 0,
  dodge_val = 0.5,
  rev_colors = TRUE,
  d_obj = NULL
)

plot_rel_biomass(
  model_lst = NULL,
  model_names = NULL,
  d_obj = NULL,
  show_arrows = TRUE,
  xlim = c(1966, year(Sys.time())),
  x_breaks = xlim[1]:xlim[2],
  x_labs_mod = 5,
  x_expansion = 3,
  tick_prop = 1,
  vjust_x_labels = -2,
  hjust_y_labels = 0,
  ylim = c(0, 3.5),
  y_breaks = seq(ylim[1], ylim[2], by = 0.5),
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  rev_colors = TRUE,
  wrap_y_label = FALSE,
  alpha = ts_ribbon_alpha,
  point_color = ts_single_model_pointcolor,
  point_size = ifelse(is_single_model, ts_single_model_pointsize, ts_pointsize),
  point_shape = ifelse(is_single_model, ts_single_model_pointshape, ts_pointshape),
  point_stroke = ifelse(is_single_model, ts_single_model_pointstroke, ts_pointstroke),
  line_width = ifelse(is_single_model, ts_single_model_linewidth, ts_linewidth),
  line_gap = ts_linegap,
  single_ribbon_lines_color = ts_single_model_linecolor,
  single_ribbon_fill = ts_single_model_ribbon_fill,
  ribbon_line_type = ifelse(is_single_model, ts_single_model_ribbon_linetype,
    ts_ribbon_linetype),
  refpt_bo_linecolor = ts_refpt_bo_linecolor,
  refpt_usr_linecolor = ts_refpt_usr_linecolor,
  refpt_lrp_linecolor = ts_refpt_lrp_linecolor,
  refpt_bo_linewidth = ts_refpt_bo_linewidth,
  refpt_usr_linewidth = ts_refpt_usr_linewidth,
  refpt_lrp_linewidth = ts_refpt_lrp_linewidth,
  refpt_bo_linetype = ts_refpt_bo_linetype,
  refpt_usr_linetype = ts_refpt_usr_linetype,
  refpt_lrp_linetype = ts_refpt_lrp_linetype,
  ...
)

plot_rel_recruitment(
  model_lst = NULL,
  model_names,
  rel_yr = 2010,
  inc_means = FALSE,
  xlim = c(1966, year(Sys.time())),
  x_breaks = xlim[1]:xlim[2],
  x_labs_mod = 5,
  x_expansion = 3,
  ylim = c(0, 1.2),
  y_breaks = seq(ylim[1], ylim[2], by = 0.1),
  y_labels = y_breaks,
  y_colors = rep("black", length(y_breaks)),
  alpha = 0.2,
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  axis_title_font_size = 14,
  axis_tick_font_size = 11,
  point_size = 1.5,
  line_width = 0.5,
  clip_cover = 2,
  single_point_color = "black",
  single_line_color = "black",
  crossbar_width = 0,
  dodge_val = 0.5,
  rev_colors = FALSE,
  d_obj = NULL
)

plot_survey_index_fits(
  model,
  model_lst = NULL,
  model_names = NULL,
  d_obj = NULL,
  show_arrows = TRUE,
  survey_type = c("age1", "age2"),
  xlim = c(1995, 2021),
  x_breaks = xlim[1]:xlim[2],
  x_labs_mod = 5,
  x_expansion = 3,
  tick_prop = 1,
  vjust_x_labels = -0.25,
  ylim = c(0, 3),
  y_breaks = seq(ylim[1], ylim[2], by = 0.5),
  leg_pos = c(0.65, 0.83),
  leg_ncol = 1,
  leg_font_size = 12,
  alpha = 1,
  point_color = ts_single_model_pointcolor,
  point_size = ifelse(is_single_model, ts_single_model_pointsize, ts_pointsize),
  point_shape = ifelse(is_single_model, ts_single_model_pointshape, ts_pointshape),
  line_width = ifelse(is_single_model, ts_single_model_linewidth, ts_linewidth),
  line_type = ts_single_model_linetype,
  line_color = ts_single_model_linecolor,
  obs_point_shape = 17,
  obs_point_size = point_size * 2,
  obs_line_type = "dashed",
  obs_line_width = 1.25,
  obs_err_line_type = "solid",
  obs_err_line_width = 1,
  obs_color = "black",
  dodge_val = 0.5,
  rev_colors = FALSE,
  ...
)

Arguments

model_lst	A list of models, each created by create_rds_file()
model_names	A vector of model names,the same length as models_lst
d_obj	If not NULL this is a list which has been pre-processed to contain all models in a format that is ready to plot. Essentially the first steps of this function have been replicated outside the function (The code inside the ⁠if(is.null(d_obj))⁠) is done to stop the Rmd process from taking forever
show_arrows	Logical. If TRUE, show arrow that point toward medians lying outside the plotting area. Also allow the lines toward those median points to be plotted outside the plotting area. If FALSE, truncate the medians that lie outside the plotting area to the maximum (or minimum) y limit and draw the lines to go through those new values
xlim	The year limits to plot
x_breaks	The year value tick marks to show for the x axis
x_labs_mod	Value for major X-axis tick marks. Every Nth tick will be longer and have a label. The first and last will be shown regardless of what this number is
x_expansion	Amount to expand the x axis. See the expand argument in ggplot2::scale_x_continuous()
tick_prop	A value that the length of the major tick marks are multiplied by. This proportion must be set by trial and error. Make sure to change vjust_x_labels so the labels are not overlapping the lines or are too far away from the lines
vjust_x_labels	Adjustment to move the x-axis tick labels and label up or down. Negative numbers move down
ylim	The y-axis minimum and maximum limits for the plot
y_breaks	The tick mark values to show for the y-axis
leg_pos	The position of the legend inside the plot. If NULL, NA, or "none", the legend will not be shown
leg_ncol	The number of columns to show in the legend
leg_font_size	The legend font size labels
dodge_bo	A value to adjust the spacing between multiple B0 points and errorbars in the case of more than one model being plotted
rev_colors	Logical. If TRUE, reverse the order of the colors in the plot. Only applies if more than one model is plotted
wrap_y_label	Logical. If TRUE, adds a newline to the y-axis label so that it doesn't get cut off
alpha	The transparency for the ribbons (credible intervals)
point_size	The size of the points used for the median lines (See ggplot2::geom_point() for more details)
point_color	The R color for the points used for the median lines (See ggplot2::geom_point() for more details)
point_shape	The R shape number for the points used for the median lines (See ggplot2::geom_point() for more details)
point_stroke	The stroke value for the points used for the median lines (See ggplot2::geom_point() for more details)
line_width	Width of the median line and errorbar line (for the B0 value) (See ggplot2::geom_path() for more details)
line_gap	Gap between the connecting lines and points for each line. See the mult parameter of ggh4x::geom_pointpath()
single_ribbon_lines_color	Line color for the ribbon limit lines (credible interval) for the case where there is only a single model to plot
single_ribbon_fill	The ribbon fill color if there is only a single model
ribbon_line_type	Line type for ribbon edges; use 0 for no line.
refpt_bo_linecolor	The line color for the B0 horizontal line
refpt_usr_linecolor	The line color for the Upper stock reference horizontal line
refpt_lrp_linecolor	The line color for the Limit Reference point horizontal line
refpt_bo_linewidth	The line width for the B0 horizontal line
refpt_usr_linewidth	The line width for the Upper stock reference horizontal line
refpt_lrp_linewidth	The line width for the Limit Reference point horizontal line
refpt_bo_linetype	The line type for the B0 horizontal line
refpt_usr_linetype	The line type for the Upper stock reference horizontal line
refpt_lrp_linetype	The line type for the Limit Reference point horizontal line
model	A model, created by create_rds_file()
cohorts	A vector of cohorts to make lines for
ages	A vector of ages to show
show_arrowheads	Logical. If TRUE show arrowheads on the ends of the lines
arrow_size	size of the arrow heads, npc units. See grid::unit() For description of npc
fore_inds	Indices of the model$ct_levels list to include as forecast catch streams in the plot
y_labels	Labels for the tick marks on the y-axis
y_colors	Colors for the tick labels on the y-axis
forecast_yrs	A vector of the forecast years
fspr40_text_x	X-axis value for the location of the FSPR=40% text
fspr40_text_y	Y-axis value for the location of the FSPR=40% text
fore_yr	The forecast year to use in the plot. Must be in forecast_yrs
colors	Colors to use for the lines in the plot
shapes	Shapes to use for the points in the plot. Must be the same length as colors
remove_x_val	A vector of values to remove labels for on the x axis. Plot first leaving this as NULL, then add values to this that overlap others
show_50_line	Logical. If TRUE, draw a horizontal line at 50\%
short	Logical. If TRUE, draw only 3 lines (decline, under B40, under B10)
dodge_val	The amount to separate lines between unique models multiple model plots
...	Arguments passed to ggplot2::geom_pointrange()
inc_means	Logical. If TRUE include the mean values in the plot. Used for recruitment plot only
relative	Logical. If TRUE, create a relative recruitment plot using the r_rel_lower, r_rel_med, r_rel_upper, r_rel_mean, and r_rel_init elements of mcmccalcs which are created in the function calc_mcmc(). In that function, the relative year is set. It is 2010 by default. To change it, edit the year in the calc_mcmc() function, and then create_rds_file() needs to be run again to create the RDS file for the model
horizontal_line_color	for relative plot, colour of the dashed line at 1
survey_type	Either age1 or age2. Used for survey index plots only
ax_title_font_size	Size of the font for the X and Y axis labels
ax_tick_font_size	Size of the font for the X and Y axis tick labels
ax_label_color	Color of the font for the X and Y axis tick and title labels

Value

a ggplot2::ggplot() object

pacific-hake/hake-assessment documentation built on Jan. 14, 2025, 9:12 p.m.