Package 'r4ss'

Description

Lines starting with #C at the top of the file are carried over to the *.ss_new files by Stock Synthesis This function modifies any existing header to add or replace lines written by r4ss that state the write time of the file.

Usage

add_file_header(filelist, con)

Arguments

Author(s)

Yukio Takeuchi, Ian G. Taylor

Description

ss3diags function to add legend to plots

Usage

add_legend(
  legendlabels,
  legendloc = "topleft",
  legendorder = NULL,
  legendncol = 1,
  legendcex = 1,
  legendsp = 0.9,
  col = NULL,
  pch = NULL,
  pt.cex = 0.7,
  lty = 1,
  lwd = 2,
  type = "l"
)

Arguments

Description

Bubble plot based on function vaguely based on bubble by Edzer Pebesma in gstat package. By default, positive values have closed bubbles and negative values have open bubbles.

Usage

bubble3(
  x,
  y,
  z,
  col = 1,
  cexZ1 = 5,
  maxsize = NULL,
  do.sqrt = TRUE,
  bg.open = gray(0.95, 0.3),
  legend = TRUE,
  legendloc = "top",
  legend.z = "default",
  legend.yadj = 1.1,
  main = "",
  cex.main = 1,
  xlab = "",
  ylab = "",
  minnbubble = 3,
  xlim = NULL,
  ylim = NULL,
  axis1 = TRUE,
  xlimextra = 1,
  add = FALSE,
  las = 1,
  allopen = TRUE
)

Arguments

Author(s)

Ian Stewart and Ian Taylor

Description

Function developed for U.S. west coast Sablefish assessment in 2019 to tune discard data or mean body weight data which are common inputs for U.S. west coast groundfish assessments but as of 2023 have not often had any data weighting method applied to them.

Usage

calc_var_adjust(data, type = c("CV", "sd"))

Arguments

Details

The calculation is based on sd_out = sqrt(mean(Obs - Exp)^2)). Added sd is calculated as sd_out - sd_in where sd_in is the mean of the input standard deviations (possibly including existing variance adjustments). When a CV adjustment is required, the sd_out is converted to CV_out by dividing by the mean of the expected values and with the added CV calculated as CV_out - CV_in.

Value

A table of input and estimated uncertainty values in units of both CV and sd including the following:

• fleet is the fleet number

• mean_out is the mean of the expected values

• mean_in is the mean of the observed values

• CV_in is the mean input CV

• sd_in is the mean input SD values (which may include variance adjustments already)

• sd_out is the SD of the observed relative to the expected values, calculated as described above

• CV_out is the CV of the observed relative to the expected, calculated as described above

• added is the value that could be added to any existing value in the "Input variance adjustments factors" section of the control file.

• type is the data type code used in "Input variance adjustments factors"

Author(s)

Kelli F. Johnson

Description

The check_exe() function first checks the specified directory dir for the specified SS3 executable name and returns the file's location if found. If it is not found in the specified directory, then it checks the PATH. Linux systems may have an existing executable utility ⁠/usr/sbin/ss⁠ in the path. If exe = "ss3" and this file is found by ⁠check_exe()``, it will be ignored based on the smaller file size relative to the SS3 executable. Linux users who want to use the workflow of having SS3 in their PATH should name the SS3 file something besides ⁠ss⁠, such as ⁠ss3orss_linux'.

Usage

check_exe(exe = "ss3", dir = getwd(), verbose = FALSE)

Arguments

Details

Check that the Stock Synthesis executable name provided in exe, an input argument to numerous r4ss functions is available in the location specified by dir or in the path.

Value

A list containing ⁠$exe⁠ and ⁠$path⁠. ⁠$exe⁠ is the cleaned version of the exe file name input. Windows systems will include ".exe" in the returned value. On Linux and Mac systems, the returned ⁠$exe⁠ will include "./" if the executable was found in the specified directory dir. This will be a single character string, unlike ⁠$path⁠ which will be a vector if the input dir is a vector. The ⁠$path⁠ element of the list includes the normalized path (or paths if dir is a vector) where the executable was found. If dir is a vector and the executable is missing from a subset of those directories, NA is returned for those elements of ⁠$path⁠. If the specified exe input is not found in any of the dir input values nor in the PATH, then the function stops with an error.

Author(s)

Kelli F. Johnson, Ian G. Taylor

See Also

run()

Examples

## Not run: 
# check for executable called "ss3" or "ss3.exe" in the PATH
check_exe()
# check for executable with a different name in the PATH
check_exe(exe = "ss_win")
# check for executable in a specific directory
check_exe(exe = "ss_linux", dir = "~/ss/ss_v3.30.19.01")

## End(Not run)

Description

Check the elements of the inputlist list used as an argument in SS_write() function.

Usage

check_inputlist(inputlist)

Arguments

Value

Either TRUE if the input list is valid, or FALSE if not, with a warning about which elements are missing.

Author(s)

Kelli F. Johnson, Ian G. Taylor

See Also

SS_write()

Description

Reads the starter.ss file to figure out the names of the control and data files, than copies those files along with starter.ss, forecast.ss, and wtatage.ss (if present) to a new directory, as specified.

Usage

copy_SS_inputs(
  dir.old = NULL,
  dir.new = NULL,
  create.dir = TRUE,
  overwrite = FALSE,
  recursive = FALSE,
  use_ss_new = FALSE,
  copy_exe = FALSE,
  copy_par = FALSE,
  dir.exe = NULL,
  verbose = TRUE
)

Arguments

Value

A logical value is invisibly returned, indicating whether all input files were copied successfully.

Author(s)

Ian G. Taylor

See Also

Other run functions: jitter(), populate_multiple_folders(), profile(), retro(), run(), tune_comps()

Examples

## Not run: 
# A theoretical example if "old_model" was present
# but expect an error
copy_SS_inputs(
  dir.old = "c:/SS/old_model",
  dir.new = "c:/SS/new_model"
)
# A working example using files stored in {r4ss}
copy_SS_inputs(
  dir.old = system.file("extdata", "simple_small", package = "r4ss"),
  dir.new = "test"
)
unlink(test, recursive = TRUE)

## End(Not run)

Description

The function has been moved to https://github.com/pfmc-assessments/rebuilder. This function was rarely used because it was specific to U.S. west coast groundfish stocks that were overfished and in a rebuilding plan. Therefore there's no need to have it available to all r4ss users.

Usage

DoProjectPlots(...)

Arguments

Author(s)

Ian G. Taylor

Description

Download and unzip the models folder stored on GitHub within the nmfs-ost/ss3-test-models repository.

Usage

download_models(
  dir = file.path("inst", "extdata"),
  branch = "main",
  overwrite = FALSE
)

Arguments

Value

Invisibly return a logical revealing whether the files were copied (TRUE) or not (FALSE). This function is used for its side effects of downloading SS3 test models.

Author(s)

Kathryn Doering

References

nmfs-ost/ss3-test-models repository

Examples

download_models(dir = getwd())
model_name <- list.files("models") # see the model names
# remove files
unlink(file.path("models"), recursive = TRUE)

Description

Rename files found with pattern by adding i to their name before the extension.

Usage

file_increment(
  path,
  i,
  verbose = FALSE,
  pattern = "^[CcPRw][a-zA-Z]+\\.sso|summary\\.sso|\\.par$"
)

Arguments

Details

The .par file, which is the only file extension searched for with the default entry that does not end in .sso, is modified differently.⁠_i.sso⁠ is added to the file name.

Value

Invisibly returns a vector of logical values specifying whether or not the file was successfully renamed.

Author(s)

Kelli F. Johnson

See Also

jitter()

Description

this was previously contained within SS_plots() and 4 of the SSplotXXX() functions.

Usage

get_areacols(areacols, nareas)

Arguments

Author(s)

Ian G. Taylor

Description

This function is used internally by SS_readdat_3.30, SS_readctl_3.30. This will identify 1st numeric data in dat (vector of string) Then this function collects lines starting "#C" from lines above 1st numeric data.

Usage

get_comments(dat, defaultComments = NULL)

Arguments

Author(s)

Yukio Takeuchi

See Also

SS_readdat, SS_readdat_3.30, SS_readctl, SS_readctl_3.30

Description

In previous versions of Stock Synthesis, the file new data file was named data.ss_new. ⁠_echo⁠ was added to the name when the file was parsed into three separate files.

Usage

get_dat_new_name(dir)

Arguments

Value

A string with the name of the data .ss_new file. If not found, will be NA. Both of strings are searched for using dir(pattern = ) and if both exist, then data_echo.ss_new is returned.

See Also

get_par_name

Description

Get the highest phase used in the control file

Usage

get_last_phase(ctl)

Arguments

Author(s)

Kathryn L. Doering

Description

In previous versions of Stock Synthesis,

Usage

get_par_name(dir, verbose = TRUE)

Arguments

Value

A string with the name of the .par file. If not found, will be NA. If multiple files exist, preference is given to ss3.par (default as of 3.30.22.1), followed by ss.par, followed by the most recently modified file with a *.par extension (choosing the first if two were modified at the same time).

See Also

get_dat_new_name

Description

Processes model results contained in the list created by SS_output() in a format that is more convenient for submission to SIS. Currently the results are returned invisibly as a list of two tables and written to a CSV file from which results could be copied into SIS. In the future some more direct link could be explored to avoid the manual copy step.

Usage

get_SIS_info(
  model,
  dir = model[["inputs"]][["dir"]],
  writecsv = TRUE,
  stock = "StockName",
  assessment_type = "Operational",
  final_year = model[["endyr"]] + 1,
  data_year = model[["endyr"]],
  month,
  sciencecenter = "NWFSC",
  Mgt_Council = "PFMC",
  SpawnOutputLabel = model[["SpawnOutputLabel"]],
  contact = "[email protected]",
  review_result = "XXXX",
  catch_input_data = "XXXX",
  abundance_input_data = "XXXX",
  bio_input_data = "XXXX",
  comp_input_data = "XXXX",
  ecosystem_linkage = "XXXX"
)

Arguments

Author(s)

Ian G. Taylor, Andi Stephens, LaTreese S. Denson

See Also

SS_output()

Examples

## Not run: 
# read the model output
model <- SS_output(
  dir = system.file("extdata/simple_small", package = "r4ss"),
  printstats = FALSE, verbose = FALSE
)
# run get_SIS_info:
info <- get_SIS_info(model, stock = "SimpleExample", month = 1)

## End(Not run)

Description

The get_ss3_exe() function uses the gh package to get either the latest release (if version = NULL) or the specified version of the Stock Synthesis executable for the appropriate operating system to the directory dir (if dir = NULL, then the executable is downloaded to the working directory). To view the version tags available go to https://github.com/nmfs-ost/ss3-source-code/tags

Usage

get_ss3_exe(dir = NULL, version = NULL)

Arguments

Details

Downloads the SS3 executable according to specified version and the user operating system.

Value

A string of the file path to the downloaded executable

Author(s)

Elizabeth F. Gugliotti

Examples

## Not run: 
get_ss3_exe()
get_ss3_exe(version = "v3.30.18")

## End(Not run)

Description

Get the tuning table

Usage

get_tuning_table(
  replist,
  fleets,
  option,
  digits = 6,
  write = TRUE,
  verbose = TRUE
)

Arguments

Description

function to add get the names of short time varying parameter lines

Usage

get_tv_parlabs(full_parms, block_design)

Arguments

Description

This function reads in all of the information contained in the .hes file. Some is needed for relaxing the covariance matrix, while the rest is recorded and rewritten to file as ADMB expects.

Usage

getADMBHessian(
  hesfile = "admodel.hes",
  File = lifecycle::deprecated(),
  FileName = lifecycle::deprecated()
)

Arguments

Value

A list with elements num.pars, hes, hybrid_bounded_flag, and scale.

Note

Explanation of the methods (in PDF form): https://github.com/admb-project/admb-examples/blob/master/admb-tricks/covariance-calculations/ADMB_Covariance_Calculations.pdf

Author(s)

Cole Monnahan

See Also

read.admbFit(), NegLogInt_Fn()

Description

Utility function to test if x is "numerically" integer wrt machine epsilon taken from example section of help of is.integer

Usage

is.wholenumber(x, tol = .Machine[["double.eps"]]^0.5)

Arguments

Description

Execute a single jittered model run

Usage

iterate_jitter(
  i,
  printlikes = TRUE,
  exe = "ss3",
  verbose = FALSE,
  init_values_src = 0,
  dir = NULL,
  extras = NULL,
  ...
)

Arguments

Value

Negative log-likelihood of one jittered model

Author(s)

James T. Thorson, Kelli F. Johnson, Ian G. Taylor, Kathryn L. Doering, Kiva L. Oken

Description

Iteratively run a Stock Synthesis model with different jittered starting parameter values based on the jitter fraction. Output files are renamed in the format Report1.sso, Report2.sso, etc.

Usage

jitter(
  dir = NULL,
  mydir = lifecycle::deprecated(),
  Intern = lifecycle::deprecated(),
  Njitter,
  printlikes = TRUE,
  jitter_fraction = NULL,
  init_values_src = NULL,
  exe = "ss3",
  verbose = FALSE,
  extras = NULL,
  ...
)

Arguments

Details

This function will loop through models using the default strategy set by the future package in the current working environment. In general, this means models will run sequentially. To run multiple models simultaneously using parallel computing, see future::plan()

Note that random number generation occurs outside of R directly in stock synthesis. When running jitters in parallel (i.e. future strategy is not sequential), no steps are taken to ensure independence of random numbers generated across cores. While the likelihood of the cores using the exact same seed is infinitesimal, random numbers may not technically be considered statistically independent. If jitter results are only used as a general heuristic for model convergence, this mild lack of independence should not matter much.

When running models in parallel, the transfer of large files leads to expensive overheads and parallel processing may not be faster. Covariance files are especially expensive to transfer, so the option extras = '-nohess' is recommended when using parallel processing.

Value

A vector of likelihoods for each jitter iteration.

Author(s)

James T. Thorson, Kelli F. Johnson, Ian G. Taylor, Kathryn L. Doering, Kiva L. Oken, Elizabeth F. Perl

See Also

Other run functions: copy_SS_inputs(), populate_multiple_folders(), profile(), retro(), run(), tune_comps()

Examples

## Not run: 
#### Run jitter from par file with arbitrary, but common, choice of 0.1
modeldir <- tail(dir(system.file("extdata", package = "r4ss"), full.names = TRUE), 1)
numjitter <- 25
jit.likes <- jitter(
  dir = modeldir, Njitter = numjitter,
  jitter_fraction = 0.1, init_values_src = 1
)

#### Run same jitter in parallel
ncores <- parallelly::availableCores(omit = 1)
future::plan(future::multisession, workers = ncores)
jit.likes <- jitter(
  dir = modeldir, Njitter = numjitter,
  jitter_fraction = 0.1, init_values_src = 1
)
future::plan(future::sequential)

#### Read in results using other r4ss functions
# (note that un-jittered model can be read using keyvec=0:numjitter)
profilemodels <- SSgetoutput(dirvec = modeldir, keyvec = 1:numjitter, getcovar = FALSE)
# summarize output
profilesummary <- SSsummarize(profilemodels)
# Likelihoods
profilesummary[["likelihoods"]][1, ]
# Parameters
profilesummary[["pars"]]

## End(Not run)

Description

Function created as an alternative to lattice package for multi-figure plots of composition data and fits from Stock Synthesis output.

Usage

make_multifig(
  ptsx,
  ptsy,
  yr,
  linesx = 0,
  linesy = 0,
  ptsSD = 0,
  sampsize = 0,
  effN = 0,
  showsampsize = TRUE,
  showeffN = TRUE,
  sampsize_label = "N=",
  effN_label = "effN=",
  sampsizeround = 1,
  maxrows = 6,
  maxcols = 6,
  rows = 1,
  cols = 1,
  fixdims = TRUE,
  main = "",
  cex.main = 1,
  xlab = "",
  ylab = "",
  size = 1,
  cexZ1 = 1.5,
  bublegend = TRUE,
  maxsize = NULL,
  do.sqrt = TRUE,
  minnbubble = 8,
  allopen = TRUE,
  xbuffer = c(0.1, 0.1),
  ybuffer = c(0, 0.15),
  yupper = NULL,
  ymin0 = TRUE,
  xlas = 0,
  ylas = NULL,
  axis1 = NULL,
  axis2 = NULL,
  axis1labs = NULL,
  linepos = 1,
  type = "o",
  polygons = TRUE,
  bars = FALSE,
  barwidth = "default",
  ptscex = 1,
  ptscol = 1,
  ptscol2 = 1,
  colvec = c(rgb(1, 0, 0, 0.7), rgb(0, 0, 1, 0.7), rgb(0.1, 0.1, 0.1, 0.7)),
  linescol = c(rgb(0, 0.8, 0, 0.7), rgb(1, 0, 0, 0.7), rgb(0, 0, 1, 0.7)),
  lty = 1,
  lwd = 2,
  pch = 1,
  nlegends = 3,
  legtext = list("yr", "sampsize", "effN"),
  legx = "default",
  legy = "default",
  legadjx = "default",
  legadjy = "default",
  legsize = c(1.2, 1),
  legfont = c(2, 1),
  venusmars = TRUE,
  sampsizeline = FALSE,
  effNline = FALSE,
  sampsizemean = NULL,
  effNmean = NULL,
  ipage = 0,
  scalebins = FALSE,
  sexvec = NULL,
  multifig_colpolygon = grey(c(0.6, 0.8, 0.7), alpha = 0.7),
  multifig_oma = NULL,
  ...
)

Arguments

Author(s)

Ian Taylor

See Also

SS_plots(),SSplotComps()

Description

Modified version of make_multifig() for multi-figure plots of sex ratio data with crude confidence intervals (+/i 1 se) and fits from Stock Synthesis output.

Usage

make_multifig_sexratio(
  dbase,
  sexratio.option = 2,
  CI = 0.75,
  sampsizeround = 1,
  maxrows = 6,
  maxcols = 6,
  rows = 1,
  cols = 1,
  fixdims = TRUE,
  main = "",
  cex.main = 1,
  xlab = "",
  ylab = "Fraction female",
  horiz_lab = "default",
  xbuffer = c(0.1, 0.1),
  ybuffer = "default",
  yupper = NULL,
  datonly = FALSE,
  showsampsize = TRUE,
  showeffN = TRUE,
  axis1 = NULL,
  axis2 = NULL,
  ptscex = 1,
  ptscol = gray(0.5),
  linescol = 4,
  lty = 1,
  lwd = 2,
  nlegends = 3,
  legtext = list("yr", "sampsize", "effN"),
  legx = "default",
  legy = "default",
  legadjx = "default",
  legadjy = "default",
  legsize = c(1.2, 1),
  legfont = c(2, 1),
  ipage = 0,
  multifig_oma = c(5, 5, 5, 2) + 0.1,
  ...
)

Arguments

Details

The SE of the sex ratio is crude and calculated as follows. First, assume a multinomial which as MLEs of proportions. Then use the delta method of the ratio F/M, using the MLE as the expected values and analytical variances and covariance between F and M. After some algebra this calculation reduces to: SE(F/M)= sqrt((f/m)^2*( (1-f)/(f*N) + (1-m)/(m*N) +2/N )). Confidence intervals created from these should be considered very crude and would not necessarily be appropriate for future alternative compositional likelihoods.

This function was derived from make_multifig and hence has a lot of overlap in functionality and arguments.

Author(s)

Cole Monnahan. Adapted from make_multifig().

See Also

SS_plots(),SSplotSexRatio()

Description

Summarize nuisance MCMC output (used in combination with mcmc.out() for key parameters).

Usage

mcmc.nuisance(
  directory = "c:/mydirectory/",
  run = "mymodel/",
  file = "posteriors.sso",
  file2 = "derived_posteriors.sso",
  bothfiles = FALSE,
  printstats = FALSE,
  burn = 0,
  header = TRUE,
  thin = 1,
  trace = 0,
  labelstrings = "all",
  columnnumbers = "all",
  sep = ""
)

Arguments

Author(s)

Ian Stewart

See Also

mcmc.out(), SSgetMCMC()

Description

Makes four panel plot showing trace plots, moving average, autocorrelations, and densities for chosen parameters from MCMC output.

Usage

mcmc.out(
  directory = "c:/mydirectory/",
  run = "mymodel/",
  file = "keyposteriors.csv",
  namefile = "postplotnames.sso",
  names = FALSE,
  headernames = TRUE,
  numparams = 1,
  closeall = TRUE,
  burn = 0,
  thin = 1,
  scatter = FALSE,
  surface = FALSE,
  surf1 = 1,
  surf2 = 2,
  stats = FALSE,
  plots = TRUE,
  header = TRUE,
  sep = ",",
  print = FALSE,
  new = T,
  colNames = NULL
)

Arguments

Value

directory, because this function is used for its plotting side effects

Author(s)

Ian Stewart, Allan Hicks (modifications)

See Also

mcmc.nuisance(), SSgetMCMC()

Examples

## Not run: 
mcmc.df <- SSgetMCMC(
  dir = "mcmcRun", writecsv = T,
  keystrings = c("NatM", "R0", "steep", "Q_extraSD"),
  nuisancestrings = c("Objective_function", "SSB_", "InitAge", "RecrDev")
)
mcmc.out("mcmcRun", run = "", numparams = 4, closeall = F)

# Or for more control
par(mar = c(5, 3.5, 0, 0.5), oma = c(0, 2.5, 0.2, 0))
mcmc.out("mcmcRun",
  run = "",
  numparams = 1,
  closeall = F,
  new = F,
  colNames = c("NatM_p_1_Fem_GP_1")
)
mtext("M (natural mortality)", side = 2, outer = T, line = 1.5, cex = 1.1)

## End(Not run)

Description

Designed to replicate like the cool-looking Figure 7 in Butterworth et al. (2003).

Usage

mountains(
  zmat,
  xvec = NULL,
  yvec = NULL,
  zscale = 3,
  rev = TRUE,
  nshades = 100,
  axes = TRUE,
  xaxs = "i",
  yaxs = "i",
  xlab = "",
  ylab = "",
  las = 1,
  addbox = FALSE,
  ...
)

Arguments

Author(s)

Ian Taylor

References

Butterworth D.S., Ianelli J.N., Hilborn R. (2003) A statistical model for stock assessment of southern bluefin tuna with temporal changes in selectivity. South African Journal of Marine Science 25:331-362.

Description

(Attempt to) perform the SS implementation of the Laplace Approximation from Thorson, Hicks and Methot (2014) ICES J. Mar. Sci.

Usage

NegLogInt_Fn(
  dir = getwd(),
  File = lifecycle::deprecated(),
  Input_SD_Group_Vec,
  CTL_linenum_List,
  ESTPAR_num_List,
  PAR_num_Vec,
  Int_Group_List = list(1),
  StartFromPar = TRUE,
  Intern = lifecycle::deprecated(),
  ReDoBiasRamp = FALSE,
  BiasRamp_linenum_Vec = NULL,
  CTL_linenum_Type = NULL,
  exe = "ss3",
  verbose = FALSE,
  ...
)

Arguments

Author(s)

James Thorson

References

Thorson, J.T., Hicks, A.C., and Methot, R.D. 2014. Random effect estimation of time-varying factors in Stock Synthesis. ICES J. Mar. Sci.

See Also

read.admbFit(), getADMBHessian()

Examples

## Not run: 
# need the full path because wd is changed in function
direc <- "C:/Models/LaplaceApprox/base"
if ("Optimization_record.txt" %in% list.files(direc)) {
  file.remove(file.path(direc, "Optimization_record.txt"))
}
Opt <- optimize(
  f = NegLogInt_Fn,
  interval = c(0.001, 0.12),
  maximum = FALSE,
  dir = direc,
  Input_SD_Group_Vec = 1,
  CTL_linenum_List = list(127:131),
  ESTPAR_num_List = list(86:205),
  Int_Group_List = 1,
  PAR_num_Vec = NA,
  Intern = TRUE
)

## End(Not run)

Description

This style of plot was officially named a "Piner Plot" at the CAPAM Selectivity Workshop, La Jolla March 2013. This is in honor of Kevin Piner's contributions to interpreting likelihood profiles. He's surely not the first person to make such a plot but the name seems to have stuck.

Usage

PinerPlot(
  summaryoutput,
  plot = TRUE,
  print = FALSE,
  component = "Length_like",
  main = "Changes in length-composition likelihoods by fleet",
  models = "all",
  fleets = "all",
  fleetnames = "default",
  profile.string = "R0",
  profile.label = expression(log(italic(R)[0])),
  exact = FALSE,
  ylab = "Change in -log-likelihood",
  col = "default",
  pch = "default",
  lty = 1,
  lty.total = 1,
  lwd = 2,
  lwd.total = 3,
  cex = 1,
  cex.total = 1.5,
  xlim = "default",
  ymax = "default",
  xaxs = "r",
  yaxs = "r",
  type = "o",
  legend = TRUE,
  legendloc = "topright",
  pwidth = 6.5,
  pheight = 5,
  punits = "in",
  res = 300,
  ptsize = 10,
  cex.main = 1,
  plotdir = NULL,
  add_cutoff = FALSE,
  cutoff_prob = 0.95,
  verbose = TRUE,
  fleetgroups = NULL,
  likelihood_type = "raw_times_lambda",
  minfraction = 0.01
)

Arguments

Author(s)

Ian G. Taylor, Kevin R. Piner, James T. Thorson

References

Kevin Piner says that he's not the originator of this idea so Athol Whitten is going to add a reference here.

See Also

Other profile functions: SSplotProfile(), profile()

Description

Given a set of x and y values and upper and lower bounds, this function plots the points with error bars. This was Written by Venables and modified to add access to ylim and contents.

Usage

plotCI(
  x,
  y = NULL,
  uiw,
  liw = uiw,
  ylo = NULL,
  yhi = NULL,
  ...,
  sfrac = 0.01,
  ymax = NULL,
  add = FALSE,
  col = "black"
)

Arguments

Author(s)

Bill Venables, Ian Stewart, Ian Taylor, John Wallace

Description

Creates a set of multiple folders and populates each with SS3 input files such as for the purpose of running a new version of SS3 for an existing set of test models.

Usage

populate_multiple_folders(
  outerdir.old,
  outerdir.new,
  create.dir = TRUE,
  overwrite = FALSE,
  use_ss_new = FALSE,
  copy_par = FALSE,
  exe.dir = NULL,
  exe.file = "ss3",
  verbose = TRUE
)

Arguments

Value

Returns a table of results indicating which directories were successfully populated with the model input files and/or executables.

Author(s)

Ian G. Taylor, Kelli F. Johnson

See Also

Other run functions: copy_SS_inputs(), jitter(), profile(), retro(), run(), tune_comps()

Examples

## Not run: 
populate_multiple_folders(
  outerdir.old = system.file("extdata", package = "r4ss"),
  outerdir.new = file.path(tempdir(), "test")
)

## End(Not run)

Description

Iteratively changes the control file for the chosen parameter. This function was formerly called SS_profile().

Usage

profile(
  dir,
  oldctlfile = "control.ss_new",
  masterctlfile = lifecycle::deprecated(),
  newctlfile = "control_modified.ss",
  linenum = NULL,
  string = NULL,
  profilevec = NULL,
  usepar = FALSE,
  globalpar = FALSE,
  parlinenum = NULL,
  parstring = NULL,
  saveoutput = TRUE,
  overwrite = TRUE,
  whichruns = NULL,
  prior_check = TRUE,
  read_like = lifecycle::deprecated(),
  exe = "ss3",
  verbose = TRUE,
  conv_criteria = 0.01,
  ...
)

Arguments

Note

The starting values used in this profile are not ideal and some models may not converge. Care should be taken in using an automated tool like this, and some models are likely to require rerunning with alternate starting values.

To run multiple models simultaneously using parallel computing, see future::plan(). However, when running models in parallel, you cannot iteratively adapt the starting values using usepar = TRUE and globalpar = FALSE. This increases the chances that some of your models do not converge.

Also, someday this function will be improved to work directly with the plotting function SSplotProfile(), but they don't yet work well together. Thus, even if profile() is used, the output should be read using SSgetoutput() or by multiple calls to SS_output() before sending to SSplotProfile().

Author(s)

Ian G. Taylor, Kathryn L. Doering, Kelli F. Johnson, Chantel R. Wetzel, James T. Thorson, Kiva L. Oken

See Also

SSgetoutput(), SS_changepars(), SS_parlines()

Other run functions: copy_SS_inputs(), jitter(), populate_multiple_folders(), retro(), run(), tune_comps()

Other profile functions: PinerPlot(), SSplotProfile()

Examples

## Not run: 

###########################################################################
# example profile
# (assumes you have an SS3 exe called "ss3.exe" or "ss3" in your PATH)
###########################################################################

# directory for "simple_small" example model included with r4ss
dir_simple_small <- file.path(
  path.package("r4ss"),
  file.path("extdata", "simple_small")
)

# create temporary directory and copy files into it
dir_prof <- file.path(tempdir(), "profile")
copy_SS_inputs(
  dir.old = dir_simple_small,
  dir.new = dir_prof,
  create.dir = TRUE,
  overwrite = TRUE,
  copy_par = TRUE,
  verbose = TRUE
)

# the following commands related to starter.ss could be done by hand
# read starter file
starter <- SS_readstarter(file.path(dir_prof, "starter.ss"))
# change control file name in the starter file
starter[["ctlfile"]] <- "control_modified.ss"
# make sure the prior likelihood is calculated
# for non-estimated quantities
starter[["prior_like"]] <- 1
# write modified starter file
SS_writestarter(starter, dir = dir_prof, overwrite = TRUE)
# vector of values to profile over
h.vec <- seq(0.3, 0.9, .1)
Nprofile <- length(h.vec)
# run profile command
prof.table <- profile(
  dir = dir_prof,
  oldctlfile = "control.ss",
  newctlfile = "control_modified.ss",
  string = "steep", # subset of parameter label
  profilevec = h.vec
)
# read the output files (with names like Report1.sso, Report2.sso, etc.)
profilemodels <- SSgetoutput(dirvec = dir_prof, keyvec = 1:Nprofile)
# summarize output
profilesummary <- SSsummarize(profilemodels)

# OPTIONAL COMMANDS TO ADD MODEL WITH PROFILE PARAMETER ESTIMATED
# (in the "simple_small" example, steepness is fixed so it doesn't
# have any impact)
MLEmodel <- SS_output(dir_simple_small, verbose = FALSE, printstats = FALSE)
profilemodels[["MLE"]] <- MLEmodel
profilesummary <- SSsummarize(profilemodels)
# END OPTIONAL COMMANDS

# plot profile using summary created above
results <- SSplotProfile(profilesummary, # summary object
  profile.string = "steep", # substring of profile parameter
  profile.label = "Stock-recruit steepness (h)"
) # axis label

# make timeseries plots comparing models in profile
SSplotComparisons(profilesummary, legendlabels = paste("h =", h.vec))

# run same profile in parallel
ncores <- parallelly::availableCores(omit = 1)
future::plan(future::multisession, workers = ncores)
prof.table <- profile(
  dir = dir_prof,
  oldctlfile = "control.ss",
  newctlfile = "control_modified.ss",
  string = "steep", # subset of parameter label
  profilevec = h.vec
)
future::plan(future::sequential)

###########################################################################
# example two-dimensional profile
# (assumes you have an SS3 exe called "ss3.exe" or "ss3" in your PATH)
###########################################################################

dir_simple_small <- file.path(
  path.package("r4ss"),
  file.path("extdata", "simple_small")
)

# create temporary directory and copy files into it
dir_prof <- file.path(tempdir(), "profile_2D")
copy_SS_inputs(
  dir.old = dir_simple_small,
  dir.new = dir_prof,
  create.dir = TRUE,
  overwrite = TRUE,
  copy_par = TRUE,
  verbose = TRUE
)


# create table of M values for females and males
par_table <- expand.grid(
  M1vec = c(0.05, 0.10, 0.15),
  M2vec = c(0.05, 0.10, 0.15)
)

# run model once to create control.ss_new with
# good starting parameter values
# exe is assumed to be in PATH, add "exe" argument if needed
run(dir_prof, extras = "-nohess")

# run profile using ss_new file as parameter source and
# overwriting original control file with new values
prof.table <- profile(
  dir = dir_prof,
  oldctlfile = "control.ss_new",
  newctlfile = "control.ss",
  string = c("NatM_uniform_Fem_GP_1", "NatM_uniform_Mal_GP_1"),
  profilevec = par_table,
  extras = "-nohess"
)

# get model output
profilemodels <- SSgetoutput(
  dirvec = dir_prof,
  keyvec = 1:nrow(par_table), getcovar = FALSE
)
n <- length(profilemodels)
profilesummary <- SSsummarize(profilemodels)

# add total likelihood (row 1) to table created above
par_table[["like"]] <- as.numeric(profilesummary[["likelihoods"]][1, 1:n])

# reshape data frame into a matrix for use with contour
like_matrix <- reshape2::acast(
  data = par_table,
  formula = M1vec ~ M2vec,
  value.var = "like"
)

# look at change relative to the minimum
# (shows small change when female and male M are equal,
# big change when they are different)
like_matrix - min(like_matrix)
#         0.05    0.1    0.15
# 0.05   6.938 32.710 121.959
# 0.1   49.706  0.000  27.678
# 0.15 154.897 44.768   5.366

## End(Not run)

Description

This function will parse the .par and .cor files to provide things like parameter estimates, standard deviations, and correlations. Required for Jim Thorson's Laplace Approximation but likely useful for other purposes.

Usage

read.admbFit(file)

Arguments

Value

List of various things from these files.

Author(s)

James Thorson

See Also

getADMBHessian(), NegLogInt_Fn()

Description

Do retrospective analyses by creating new directories, copying model files, and iteratively changing the starter file to set the number of years of data to exclude. Note that there was a bug for retrospectives in 3.30.01; the user should update their model to a newer version of Stock Synthesis to run retrospectives. To run retrospective models in parallel, use future::plan() before running retro().

Usage

retro(
  dir = getwd(),
  masterdir = lifecycle::deprecated(),
  oldsubdir = "",
  newsubdir = "retrospectives",
  subdirstart = "retro",
  years = 0:-5,
  overwrite = TRUE,
  RemoveBlocks = FALSE,
  verbose = FALSE,
  exe = "ss3",
  ...
)

Arguments

Author(s)

Ian G. Taylor, James T. Thorson, Kathryn L. Doering, Kiva L. Oken

See Also

SSgetoutput()

Other run functions: copy_SS_inputs(), jitter(), populate_multiple_folders(), profile(), run(), tune_comps()

Examples

## Not run: 
# note: don't run this in your main directory--make a copy in case something
# goes wrong
mydir <- "C:/Simple"

## retrospective analyses
retro(
  dir = mydir,
  years = 0:-5
)

retroModels <- SSgetoutput(
  dirvec = file.path(mydir, "retrospectives", paste("retro", 0:-5, sep = ""))
)
retroSummary <- SSsummarize(retroModels)
endyrvec <- retroSummary[["endyrs"]] + 0:-5
SSplotComparisons(retroSummary,
  endyrvec = endyrvec,
  legendlabels = paste("Data", 0:-5, "years")
)

## run retrospectives in parallel
ncores <- parallelly::availableCores(omit = 1)
future::plan(future::multisession, workers = ncores)
retro(
  dir = mydir,
  years = 0:-5
)
future::plan(future::sequential)

## End(Not run)

Description

A subset of rich.colors by Arni Magnusson from the gplots package, with the addition of alpha transparency (which is now available in the gplots version as well)

Usage

rich.colors.short(n, alpha = 1)

Arguments

Author(s)

Arni Magnusson, Ian Taylor

Description

The run() function checks for the executable via check_exe(). This involves first checking the specified directory dir for the specified SS3 executable name. If it is not found in the specified directory, then it checks the PATH. Linux systems may have an existing executable utility ⁠/usr/sbin/ss⁠ in the path. If exe = "ss3" and this file is found by check_exe(), it will be ignored based on the smaller file size relative to the SS3 executable. Linux users who want to use the workflow of having SS3 in their PATH should name the SS3 file something besides ss, such as ss3 or ss_linux.

Usage

run(
  dir = getwd(),
  exe = "ss3",
  extras = "",
  skipfinished = TRUE,
  show_in_console = FALSE,
  console_output_file = "console.output.txt",
  verbose = TRUE
)

Arguments

Details

Checks for presence of a Stock Synthesis executable and then runs the model with any additional arguments specified by extras.

Value

Returns one of five messages: "ran model", "model run failed", "unknown run status", "not a directory", or "contained Report.sso".

Author(s)

Ian G. Taylor, Kathryn L. Doering, Kelli F. Johnson

See Also

Other run functions: copy_SS_inputs(), jitter(), populate_multiple_folders(), profile(), retro(), tune_comps()

Examples

## Not run: 
dir <- system.file("extdata", "simple_small", package = "r4ss")
r4ss::run(dir = dir)

## End(Not run)

Description

this was previously contained within each of the SSplotXXX() functions. It (1) translates the not-quite-matching specifications for the image to the values needed by png(), then (2) returns the plotinfo data.frame (which exists within each function which calls this) after adding a row with the filename and caption for each new plot Note: this just opens the png device which needs to be closed via dev.off() outside this function.

Usage

save_png(
  plotinfo,
  file,
  plotdir,
  pwidth,
  pheight,
  punits,
  res,
  ptsize,
  caption = NA,
  alt_text = NA,
  filenameprefix = NA
)

Arguments

Author(s)

Ian G. Taylor

Description

This app is hosted at https://connect.fisheries.noaa.gov/ss3-helper/

Usage

selShapes()

Author(s)

Allan C. Hicks, Andrea M. Havron, Ian G. Taylor, Kathryn L. Doering

inspired by tcl/tk code written by Tommy Garrison

Description

Loops over a subset of control file to change parameter lines. Current initial value, lower and upper bounds, and phase can be modified, but function could be expanded to control other columns. Depends on SS_parlines(). Used by profile() and the ss3sim package.

Usage

SS_changepars(
  dir = NULL,
  ctlfile = "control.ss_new",
  newctlfile = "control_modified.ss",
  linenums = NULL,
  strings = NULL,
  newvals = NULL,
  repeat.vals = FALSE,
  newlos = NULL,
  newhis = NULL,
  newprior = NULL,
  newprsd = NULL,
  newprtype = NULL,
  estimate = NULL,
  verbose = TRUE,
  newphs = NULL
)

Arguments

Author(s)

Ian Taylor, Christine Stawitz, Chantel Wetzel, Kiva L. Oken

See Also

SS_parlines(), profile()

Examples

## Not run: 
SS_changepars(
  dir = "C:/ss/SSv3.30.03.05_May11/Simple - Copy",
  strings = c("steep", "sigmaR"), newvals = c(.4, .6)
)
## parameter names in control file matching input vector 'strings' (n=2):
## [1] "SR_BH_steep" "SR_sigmaR"
## These are the ctl file lines as they currently exist:
##     LO HI     INIT PRIOR PR_type SD PHASE env_var&link dev_link dev_minyr dev_maxyr
## 95 0.2  1 0.613717   0.7    0.05  1     4       0       0         0         0
## 96 0.0  2 0.600000   0.8    0.80  0    -4       0       0         0         0
##        dev_PH Block Block_Fxn       Label Linenum
## 95          0     0         0 SR_BH_steep      95
## 96          0     0         0   SR_sigmaR      96
## line numbers in control file (n=2):
## [1] 95 96
##
## wrote new file to control_modified.ss with the following changes:
##    oldvals newvals oldphase newphase oldlos newlos oldhis newhis       comment
## 1 0.613717     0.4        4       -4    0.2    0.2      1      1 # SR_BH_steep
## 2 0.600000     0.6       -4       -4    0.0    0.0      2      2   # SR_sigmaR

## End(Not run)

Description

Values of total catch, spawning output, and fraction unfished are extracted from the forecast years of a time series table for inclusion in a decision table.

Usage

SS_decision_table_stuff(replist, yrs = 2021:2032, digits = c(0, 0, 3))

Arguments

Author(s)

Ian G. Taylor

See Also

SS_ForeCatch()

Description

SS_doRetro() has been renamed as retro(). See https://github.com/r4ss/r4ss/issues/723 for more details.

Usage

SS_doRetro(...)

Arguments

Author(s)

Ian G. Taylor

See Also

retro()

Description

Uses standard error of estimated recruitment deviates to estimate the 5 controls (Methot and Taylor, 2011) for bias adjustment in Stock Synthesis.

Usage

SS_fitbiasramp(
  replist,
  verbose = FALSE,
  startvalues = NULL,
  method = "BFGS",
  twoplots = TRUE,
  transform = FALSE,
  plot = TRUE,
  print = FALSE,
  plotdir = "default",
  shownew = TRUE,
  oldctl = NULL,
  newctl = NULL,
  altmethod = "nlminb",
  exclude_forecast = FALSE,
  pwidth = 6.5,
  pheight = 5,
  punits = "in",
  ptsize = 10,
  res = 300,
  cex.main = 1
)

Arguments

Details

Implementation of the bias adjustment ramp within Stock Synthesis increases the likelihood that the estimated recruitment events, which are log-normally distributed, are mean unbiased and comparable to results from Markov chain Monte Carlo estimation routines (Methot and Taylor, 2011). Options to account for the fact that data typically do not equally represent all modelled time periods are as follows:

1. fix the bias adjustment parameters at best-guess values informed by a previous assessment or model run;

2. fix values based on data availability, such that the start of the ramp aligns with the availability of composition data, the ramp down begins the last year those data are informative about recruitment, and the adjustment level is informed by life history;

3. set the adjustment level to 1.0 for all years to mimic how it was handled it Stock Synthesis prior to 2009; or

4. set the adjustment level to 0.0 for all years, but this last option is not recommended because it will lead to biased results.

Author(s)

Ian Taylor

References

Methot, R.D. and Taylor, I.G., 2011. Adjusting for bias due to variability of estimated recruitments in fishery assessment models. Can. J. Fish. Aquat. Sci., 68:1744-1760.

See Also

SS_output()

Description

Processing values of dead or retained biomass from timeseries output to fit the format required at the bottom of the forecast file. This can be used to map the catches resulting from forecasting with a particular harvest control rule into a model representing a different state of nature. This is a common task for US west coast groundfish but might be useful elsewhere.

Usage

SS_ForeCatch(
  replist,
  yrs = 2021:2032,
  average = FALSE,
  avg.yrs = 2016:2020,
  total = NULL,
  digits = 2,
  dead = TRUE,
  zeros = FALSE
)

Arguments

Author(s)

Ian G. Taylor

See Also

SS_readforecast(), SS_readforecast()

Examples

## Not run: 
# create table based on average over past 5 years
SS_ForeCatch(base, # object created by SS_output
  yrs = 2021:2022, # years with fixed catch
  average = TRUE, # catch by fleet from average catch
  avg.yrs = 2014:2018
) # use average of catches over past 5 years

# create table with pre-defined totals where the first 2 years
# are based on current harvest specifications and the next 10 are set to some
# new value (with ratio among fleets based on average over past 5 years)
SS_ForeCatch(base, # object created by SS_output
  yrs = 2021:2022, # years with fixed catch
  average = TRUE, # catch by fleet from average catch
  avg.yrs = 2016:2020, # use average of catches over past 5 years
  total = c(rep(241.3, 2), rep(300, 10))
) # total

# create table based on harvest control rule projection in SS
# that can be mapped into an alternative state of nature
SS_ForeCatch(low_state, # object created by SS_output for low state
  yrs = 2021:2032, # forecast period after fixed ACL years
  average = FALSE
) # use values forecast in SS, not historic catch

## End(Not run)

Description

Writes a set of HTML files with tabbed navigation between them. Depends on SS_plots() with settings in place to write figures to PNG files. Should open main file in default browser automatically.

Usage

SS_html(
  replist = NULL,
  plotdir = NULL,
  plotInfoTable = NULL,
  title = "SS Output",
  width = 500,
  openfile = TRUE,
  multimodel = FALSE,
  filenotes = NULL,
  verbose = TRUE
)

Arguments

Note

By default, this function will look in the directory where PNG files were created for CSV files with the name 'plotInfoTable...' written by 'SS_plots. HTML files are written to link to these plots and put in the same directory. Please provide feedback on any bugs, annoyances, or suggestions for improvement.

Author(s)

Ian Taylor

See Also

SS_plots(), SS_output()

Description

Creates html tables that show diagnostic outputs, including status checks, gradients, and correlations.

Usage

SS_makeHTMLdiagnostictable(
  replist,
  plotdir = NULL,
  gradmax = 0.001,
  ncor = 50,
  cormax = 0.95,
  cormin = 0.01
)

Arguments

Value

a three-element vector; the first element is the name of the html table file, the second is the table caption, and the third is the category of output type

Author(s)

Christine Stawitz

See Also

SS_plots(), SS_output(), SS_html()

Description

Reads the Report.sso and (optionally) the covar.sso, CompReport.sso and other files produced by Stock Synthesis and formats the important content of these files into a list in the R workspace. A few statistics unavailable elsewhere are taken from the .par file. Summary information and statistics can be returned to the R console or just contained within the list produced by this function.

Usage

SS_output(
  dir = "C:/myfiles/mymodels/myrun/",
  dir.mcmc = NULL,
  repfile = "Report.sso",
  compfile = "CompReport.sso",
  covarfile = "covar.sso",
  forefile = "Forecast-report.sso",
  wtfile = "wtatage.ss_new",
  warnfile = "warning.sso",
  ncols = lifecycle::deprecated(),
  forecast = TRUE,
  warn = TRUE,
  covar = TRUE,
  readwt = TRUE,
  verbose = TRUE,
  printstats = TRUE,
  hidewarn = FALSE,
  NoCompOK = TRUE,
  aalmaxbinrange = 4
)

Arguments

Value

Many values are returned. Complete list would be quite long, but should probably be created at some point in the future.

Author(s)

Ian Stewart, Ian Taylor

See Also

SS_plots()

Examples

## Not run: 
# read model output
myreplist <- SS_output(dir = "c:/SS/Simple/")
# make a bunch of plots
SS_plots(myreplist)

# read model output and also read MCMC results (if run), which in
# this case would be stored in c:/SS/Simple/mcmc/
myreplist <- SS_output(dir = "c:/SS/Simple/", dir.mcmc = "mcmc")

## End(Not run)

Description

A simple function which takes as input the full path and filename of a control file for input to Stock Synthesis. Ideally, a Control.SS_New file will be used, so that it represents what SS thinks the inputs are, and not what the user thinks the inputs are.

Usage

SS_parlines(
  ctlfile = "control.ss_new",
  dir = NULL,
  version = "3.30",
  verbose = TRUE,
  active = FALSE
)

Arguments

Details

It returns a table which should contain one line for each parameter in the model. Currently, only the first 7 values are returned, because all parameters have those values. In the future, extended parameter lines could be returned.

Parameter lines are identified as those which have 7 or 14 numeric elements followed by a non-numeric element. It's possible that this system could break down under certain circumstances

Author(s)

Ian Taylor

See Also

SS_changepars(), SS_readctl(), SS_readctl_3.24()

Examples

## Not run: 
parlines <- SS_parlines(ctlfile = "c:/ss/Simple/Control.SS_New")
head(parlines)
#       LO    HI     INIT PRIOR PR_type   SD PHASE              Label Line_num
# 42  0.05  0.15  0.10000  0.10       0  0.8    -3  NatM_p_1_Fem_GP_1       42
# 43  0.05  0.15  0.10000  0.10       0  0.8    -3  NatM_p_2_Fem_GP_1       43
# 44  1.00 45.00 32.28100 36.00       0 10.0     2 L_at_Amin_Fem_GP_1       44
# 45 40.00 90.00 71.34260 70.00       0 10.0     4 L_at_Amax_Fem_GP_1       45
# 46  0.05  0.25  0.15199  0.15       0  0.8     4 VonBert_K_Fem_GP_1       46
# 47  0.05  0.25  0.10000  0.10       0  0.8    -3  CV_young_Fem_GP_1       47

## End(Not run)

Description

Creates a user-chosen set of plots, including biological quantities, time series, and fits to data. Plots are sent to R GUI, single PDF file, or multiple PNG files. This is now just a wrapper which calls on separate functions to make all the plots.

Usage

SS_plots(
  replist = NULL,
  plot = 1:26,
  pdf = FALSE,
  png = TRUE,
  html = png,
  printfolder = "plots",
  dir = "default",
  fleets = "all",
  areas = "all",
  fleetnames = "default",
  fleetcols = "default",
  fleetlty = 1,
  fleetpch = 1,
  lwd = 1,
  areacols = NULL,
  areanames = "default",
  verbose = TRUE,
  uncertainty = TRUE,
  forecastplot = FALSE,
  datplot = TRUE,
  Natageplot = TRUE,
  samplesizeplots = TRUE,
  compresidplots = TRUE,
  comp.yupper = 0.4,
  sprtarg = "default",
  btarg = "default",
  minbthresh = "default",
  pntscalar = NULL,
  bub.scale.pearson = 1.5,
  bub.scale.dat = 3,
  pntscalar.nums = 2.6,
  pntscalar.tags = 2.6,
  minnbubble = 8,
  aalyear = -1,
  aalbin = -1,
  aalresids = TRUE,
  maxneff = 5000,
  cohortlines = c(),
  smooth = TRUE,
  showsampsize = TRUE,
  showeffN = TRUE,
  sampsizeline = FALSE,
  effNline = FALSE,
  showlegend = TRUE,
  pwidth = 6.5,
  pheight = 4,
  pheight_tall = 6.5,
  punits = "in",
  ptsize = 10,
  res = 300,
  mainTitle = FALSE,
  cex.main = 1,
  selexlines = 1:6,
  rows = 1,
  cols = 1,
  maxrows = 6,
  maxcols = 4,
  maxrows2 = 4,
  maxcols2 = 4,
  andrerows = 4,
  tagrows = 3,
  tagcols = 3,
  parrows = 4,
  parcols = 2,
  fixdims = TRUE,
  new = TRUE,
  SSplotDatMargin = 8,
  filenotes = NULL,
  catchasnumbers = NULL,
  catchbars = TRUE,
  legendloc = "topleft",
  minyr = -Inf,
  maxyr = Inf,
  sexes = "all",
  scalebins = FALSE,
  scalebubbles = FALSE,
  tslabels = NULL,
  catlabels = NULL,
  maxsize = 1,
  showmle = TRUE,
  showpost = TRUE,
  showprior = TRUE,
  showinit = TRUE,
  showdev = FALSE,
  fitrange = FALSE,
  ...
)

Arguments

Author(s)

Ian Stewart, Ian Taylor

References

Walters, Hilborn, and Christensen, 2008, Surplus production dynamics in declining and recovering fish populations. Can. J. Fish. Aquat. Sci. 65: 2536-2551.

See Also

SS_output(), SSplotBiology(), SSplotCatch(), SSplotComps(), SSplotDiscard(), SSplotIndices(), SSplotMnwt(), SSplotNumbers(), SSplotRecdevs(), SSplotSelex(), SSplotSpawnrecruit(), SSplotSPR(), SSplotTags(), SSplotTimeseries(), SSplotYield()

Description

SS_profile() has been renamed as profile(). See https://github.com/r4ss/r4ss/issues/723 for more details.

Usage

SS_profile(...)

Arguments

Author(s)

Ian G. Taylor

See Also

profile()

Description

Read all the input files for a Stock Synthesis model into R as a list object. These files will be in a single directory on your machine, i.e., dir. Functionality comes from the ⁠r4ss::SS_read*()⁠ functions. This function simplifies the number of lines of code you need to write by using all of the read functions to read in the starter, control, data, and forecast files and if requested, the weight-at-age file. The starter file is helpful because it provides names for the control and data files.

Usage

SS_read(dir = getwd(), ss_new = FALSE, verbose = FALSE)

Arguments

Value

An invisible list is returned. The first element (dir) is the directory that was provided in the argument dir. The second element (path) is the result of normalizePath(dir), which gives the full path. The remaining four to six elements are list objects from reading in the following input files:

• data

• control

• starter

• forecast

• wtatage (will be NULL if not required by the model)

• par (will be NULL if not required by model or if control and par do not match)

Author(s)

Ian G. Taylor, Kelli F. Johnson

See Also

SS_write() can be used to write the input files using the list created by this function.

Other read/write functions: SS_readctl(), SS_readdat(), SS_readforecast(), SS_readstarter(), SS_write(), SS_writectl(), SS_writedat(), SS_writeforecast(), SS_writestarter()

Examples

# Read in the 'simple' example model stored in {r4ss}
inputs <- SS_read(
  dir = system.file("extdata", "simple_small", package = "r4ss")
)
# Read in an example from GitHub stored in ss3-user-examples,
# wrapped in `dontrun` because it requires an Internet connection
## Not run: 
webexample <- SS_read(dir = file.path(
  "https://raw.githubusercontent.com",
  "nmfs-ost",
  "ss3-user-examples",
  "main",
  "model_files",
  "simple_long_wtatage"
))

## End(Not run)

Description

read Stock Synthesis ss_summary.sso file into list object in R

Usage

SS_read_summary(file = "ss_summary.sso", verbose = FALSE)

Arguments

Value

Output will be a list with four elements, header, likelihoods, parameters, and derived_quants. Each is a data frame with rownames indicating the quantity shown in each row.

Author(s)

Ian Taylor

See Also

SS_output(), SS_readforecast(), SS_readdat(), SS_readstarter()

Examples

## Not run: 
summary <- SS_read_summary(file = "c:/mymodel/ss_summary.sso")

## End(Not run)

Description

Read control file from Stock Synthesis (SS3) into R as a list object. This function acts as a wrapper for version-specific SS_readctl_ functions. But all version-specific functions prior to 3.30 have been deprecated, so this function primarily calls SS_readctl_3.30(). Input arguments that do not pertain to the version of your control file can be left at their default values.

Usage

SS_readctl(
  file,
  version = "3.30",
  verbose = FALSE,
  use_datlist = TRUE,
  datlist = file.path(dirname(file), "data_echo.ss_new"),
  nseas = NULL,
  N_areas = NULL,
  Nages = NULL,
  Nsexes = NULL,
  Npopbins = NA,
  Nfleets = NULL,
  Nfleet = NULL,
  Do_AgeKey = NULL,
  Nsurveys = NULL,
  N_tag_groups = NULL,
  N_CPUE_obs = NULL,
  catch_mult_fleets = NULL,
  predM_fleets = NULL,
  Ntag_fleets = NULL,
  N_rows_equil_catch = NULL,
  N_dirichlet_parms = NULL,
  ptype = lifecycle::deprecated()
)

Arguments