SAMtool (version 1.7.0)

plot_composition: Plot composition data

Description

Plots annual length or age composition data.

Usage

plot_composition(
  Year = 1:nrow(obs),
  obs,
  fit = NULL,
  plot_type = c("annual", "bubble_data", "bubble_residuals", "mean", "heat_residuals",
    "hist_residuals"),
  N = rowSums(obs),
  CAL_bins = NULL,
  ages = NULL,
  ind = 1:nrow(obs),
  annual_ylab = "Frequency",
  annual_yscale = c("proportions", "raw"),
  bubble_adj = 1.5,
  bubble_color = c("#99999999", "white"),
  fit_linewidth = 3,
  fit_color = "red"
)

Value

Plots depending on plot_type. Invisibly returns a matrix or list of values that were plotted.

Arguments

Year

A vector of years.

obs

A matrix of either length or age composition data. For lengths, rows and columns should index years and length bin, respectively. For ages, rows and columns should index years and age, respectively.

fit

A matrix of predicted length or age composition from an assessment model. Same dimensions as obs.

plot_type

Indicates which plots to create. Options include annual distributions, bubble plot of the data, and bubble plot of the Pearson residuals, and annual means.

N

Annual sample sizes. Vector of length nrow(obs).

CAL_bins

A vector of lengths corresponding to the columns in obs. and fit. Ignored for age data.

ages

An optional vector of ages corresponding to the columns in obs.

ind

A numeric vector for plotting a subset of rows (which indexes year) of obs and fit.

annual_ylab

Character string for y-axis label when plot_type = "annual".

annual_yscale

For annual composition plots (plot_type = "annual"), whether the raw values ("raw") or frequencies ("proportions") are plotted.

bubble_adj

Numeric, for adjusting the relative size of bubbles in bubble plots (larger number = larger bubbles).

bubble_color

Colors for negative and positive residuals, respectively, for bubble plots.

fit_linewidth

Argument lwd for fitted line.

fit_color

Color of fitted line.

Author

Q. Huynh

Examples

plot_composition(obs = SimulatedData@CAA[1, 1:16, ])
plot_composition(
  obs = SimulatedData@CAA[1, , ], 
  plot_type = "bubble_data", 
  ages = 0:SimulatedData@MaxAge
)
                 
SCA_fit <- SCA(x = 2, Data = SimulatedData)
plot_composition(
  obs = SimulatedData@CAA[1, , ], fit = SCA_fit@C_at_age,
  plot_type = "mean", ages = 0:SimulatedData@MaxAge
)

plot_composition(
  obs = SimulatedData@CAA[1, 1:16, ], fit = SCA_fit@C_at_age[1:16, ],
  plot_type = "annual", ages = 0:SimulatedData@MaxAge
)

plot_composition(
  obs = SimulatedData@CAA[1, , ], fit = SCA_fit@C_at_age,
  plot_type = "bubble_residuals", ages = 0:SimulatedData@MaxAge
)

plot_composition(
  obs = SimulatedData@CAA[1, , ], fit = SCA_fit@C_at_age,
  plot_type = "heat_residuals", ages = 0:SimulatedData@MaxAge
)

plot_composition(
  obs = SimulatedData@CAA[1, , ], fit = SCA_fit@C_at_age,
  plot_type = "hist_residuals", ages = 0:SimulatedData@MaxAge
)