plot.tag.attrition: Plot the observed and predicted tag attrition.

View source: R/plot.tag.attrition.R

plot.tag.attritionR Documentation

Plot the observed and predicted tag attrition.


Plot the observed and predicted tag recaptures against time at liberty by tagging program, or all tagging programs combined. The plot is either a time series of the difference between the observed and predicted, or a time series of the recaptures. A loess smoother is put through the differences.


  tagdat.names = NULL,
  facet = "program",
  plot.diff = TRUE,
  scale.diff = TRUE,
  show.legend = TRUE,
  show.points = FALSE,
  palette.func = default.model.colours,



A list, or an individual data.frame, of tagging data created by the function.


A vector of character strings naming the models for plotting purposes. If not supplied, model names will be taken from the names in the tagdat.list (if available) or generated automatically.


What variable do you want to group by: "none" (no grouping), "program" (by tagging program - default), "region" (by recapture region).


Do you want to plot the difference between the observed and predicted, or a time series of recaptures? TRUE (default) or FALSE.


If TRUE, the difference between observed and predicted is scaled by the mean number of observed returns.


Do you want to show the plot legend, TRUE (default) or FALSE.


Do you want to show points as well as the smoother for the difference plots? Default is FALSE.


A function to determine the colours of the models. The default palette has the reference model in black. It is possible to determine your own palette function. Two functions currently exist: default.model.colours() and colourblind.model.colours().


Path to the directory where the outputs will be saved

Name stem for the output, useful when saving many model outputs in the same directory


Passes extra arguments to the palette function. Use the argument all.model.names to ensure consistency of model colours when plotting a subset of models.

PacificCommunity/ofp-sam-diags4MFCL documentation built on Nov. 7, 2022, 11:21 p.m.