R code from: Diagnosing common sources of lack of fit to composition data in fisheries stock assessment models using One-Step-Ahead (OSA) residuals

Fisheries stock assessments often include age- and size-composition data
to estimate recruitment strengths, mortality rates and management
quantities. Compositions inherently have correlation among the categories,
and therefore residuals are not independent. One-Step-Ahead (OSA)
residuals have been proposed as a replacement for the commonly used (but
incorrectly interpreted) Pearson residuals; however, there is no clear
best practice for diagnosing model fit when using OSA residuals. We use a
simple example to illustrate common sources of model-misspecification and
impacts on statistical and visual diagnostics. We find that visual
inspection of model fit aggregated across all observations reliably
identifies many types of misspecification, visual inspection of Pearson
residuals can reveal further lack of fit, and statistical analysis of OSA
residuals provides for objective evaluation of both lack-of-fit and
overall data weighting. The power to detect model misspecification depends
on the sample size, the number of age bins, and the number of years of
data. By illustrating common problems when the correct answer is known,
this work provides a guideline for model diagnostics using OSA residuals
in more complex settings. This R code will reproduce the simulated data
and analysis; each of the figures in the paper are also created.