Function reference • SAMtool

Rapid Conditioning Model

A population model intended for conditioning operating models from data-sparse to data-rich applications.

check_RCMdata() RCM(): Rapid Conditioning Model (RCM)

RCM2MOM(): Convert RCM to a multi-fleet operating model (MOM)

Management procedures

Make a management procedure from an assessment and control rule or use a suite of pre-made MPs.

make_interim_MP() make_projection_MP() make_MP(): Make a custom management procedure (MP)

SCA_MSY() SCA_75MSY() SCA_4010() DDSS_MSY() DDSS_75MSY() DDSS_4010() SP_MSY() SP_75MSY() SP_4010() SSS_MSY() SSS_75MSY() SSS_4010(): Model-based management procedures

Population models

Assessment models that can be fitted with a Data object for closed-loop simulation.

cDD() cDD_SS(): Continuous Delay-differential assessment model

DD_TMB() DD_SS(): Delay - Difference Stock Assessment in TMB

SCA() SCA2() SCA_Pope(): Statistical catch-at-age (SCA) model

SCA_CAL(): Age-structured model using fishery length composition

SCA_DDM(): SCA models with time-varying natural mortality

SCA_RWM(): SCA with random walk in M

SP() SP_SS() SP_Fox(): Surplus production model with FMSY and MSY as leading parameters

RCM_assess(): The rapid conditioning model as an assessment function

SSS(): Simple Stock Synthesis

Shortcut() Shortcut2() Perfect(): Assessment emulator as a shortcut to model fitting in closed-loop simulation

VPA(): Virtual population analysis (VPA)

projection(): Projections for assessment models

Harvest control rules

Functions to pair with an assessment model to create a catch-based managment procedure.

HCR_MSY(): Harvest control rule to fish at some fraction of maximum sustainable yield

HCR_escapement(): Fixed escapement harvest control rule

HCR_fixedF(): Simple fixed F harvest control rule

HCR_segment(): Segmented harvest control rules

HCR_ramp() HCR40_10() HCR60_20() HCR80_40MSY(): Linearly ramped harvest control rules

Utility functions

Functions to evaluate behavior of assessment models.

compare_models(): Compare output from several assessment models

diagnostic() diagnostic_AM(): Diagnostic of assessments in MSE: did Assess models converge during MSE?

posterior() RCMstan(): Sample posterior of TMB models in SAMtool

prelim_AM(): Preliminary Assessments in MSE

profile(<Assessment>) profile(<RCModel>): Profile likelihood of assessment models

simulate(): Generate simulated data from TMB models in SAMtool

retrospective(): Retrospective analysis of assessment models

retrospective_AM(): retrospective_AM (retrospective of Assessment model in MSE)

Plotting functions

Generate summary and figures of assessment output.

summary(<Assessment>): Summary of Assessment object

plot(<RCModel>,<missing>) compare_RCM(): Plot RCM scope output

plot(<Assessment>,<missing>) plot(<Assessment>,<retro>): Plot Assessment object

plot(<prof>,<missing>): Plot profile object

plot(<retro>,<missing>) summary(<retro>): Methods for retro object

plot_SR(): Plot stock-recruitment function

plot_betavar(): Plots a beta variable

plot_composition(): Plot composition data

plot_lognormalvar(): Plots a lognormal variable

plot_residuals(): Plot residuals

plot_steepness(): Plots probability distribution function of stock-recruit steepness

plot_timeseries(): Plot time series of data

Data indicators

Calculate the statistical power of data indicators, generated in closed-loop simulation, to detect differences between operating models (Carruthers and Hordyk 2018).

PRBcalc(): Calculate mahalanobis distance (null and alternative MSEs) and statistical power for all MPs in an MSE

Probs(): Calculates mahalanobis distance and rejection of the Null operating model

getinds(): Characterize posterior predictive data

mahplot(): Plot statistical power of the indicator with increasing time blocks

plot_crosscorr(): Produce a cross-correlation plot of the derived data arising from getinds(MSE_object)