Type: Package
Title: Bootstrap-Based Methods for the Study of Fish Stocks and Aquatic Populations
Version: 1.0.2
Date: 2025-07-02
Description: A suite of bootstrap-based models and tools for analyzing fish stocks and aquatic populations. Designed for ecologists and fisheries scientists, it supports data from length-frequency distributions, tag-and-recapture studies, and hard structure readings (e.g., otoliths). See Schwamborn et al., 2019 <doi:10.1016/j.ecolmodel.2018.12.001> for background. The package includes functions for bootstrapped fitting of growth curves and plotting.
Depends: R (≥ 4.1.0)
Imports: doParallel, parallel, foreach, grDevices, graphics, stats, utils, ks, TropFishR, fishmethods
License: GPL-3
BugReports: https://github.com/rschwamborn/fishboot/issues
URL: https://github.com/rschwamborn/fishboot
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-02 16:00:27 UTC; RALF
Author: Ralf Schwamborn ORCID iD [aut, cre], Tobias K. Mildenberger [aut], Marc H. Taylor [aut], Margit Wilhelm ORCID iD [aut], Wencheng Lau-Medrano ORCID iD [ctb]
Maintainer: Ralf Schwamborn <ralf.schwamborn@ufpe.br>
Repository: CRAN
Date/Publication: 2025-07-02 16:20:07 UTC

Bootstrap-based methods for the study of fish stocks and aquatic populations

Description

The fishboot package contains a suite of new bootstrap-based models and software tools for the study of fish stocks and aquatic populations.

Author(s)

Maintainer: Ralf Schwamborn ralf.schwamborn@ufpe.br (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Bootstraped ELEFAN_GA

Description

This function performs a bootstrapped fitting of a von Bertalanffy growth function (VBGF) via the ELEFAN_GA function. Most of the arguments are simply passed to the function within many permutations (resampling) of the original lfq data. As the original function, ELEFAN_GA also conducts Electronic LEngth Frequency ANalysis using a genetic algorithm (GA) to estimate growth parameters. Partial (repeated fitting on original data) and full bootstrap (with resampling) routines are possible, depending on resample.

Usage

ELEFAN_GA_boot(
  lfq,
  seasonalised = FALSE,
  low_par = NULL,
  up_par = NULL,
  popSize = 50,
  maxiter = 100,
  run = maxiter,
  parallel = FALSE,
  pmutation = 0.1,
  pcrossover = 0.8,
  elitism = base::max(1, round(popSize * 0.05)),
  MA = 5,
  addl.sqrt = FALSE,
  agemax = NULL,
  ...,
  seed = NULL,
  nresamp = 10,
  resample = TRUE,
  outfile = NA
)

Arguments

lfq

a length frequency object of the class lfq (see lfqCreate).

seasonalised

logical; indicating if the seasonalised von Bertalanffy growth function should be applied (default: FALSE).

low_par

a list providing the minimum of the search space in case of real-valued or permutation encoded optimizations. When set to NULL the following default values are used:

  • Linf length infinity in cm (default is calculated from maximum length class in the data),

  • K curving coefficient (default: 0.01),

  • t_anchor time point anchoring growth curves in year-length coordinate system, corresponds to peak spawning month (range: 0 to 1, default: 0),

  • C amplitude of growth oscillation (range: 0 to 1, default: 0),

  • ts summer point (ts = WP - 0.5) (range: 0 to 1, default: 0);

up_par

a list providing the maximum of the search space in case of real-valued or permutation encoded optimizations. When set to NULL the following default values are used:

  • Linf length infinity in cm (default is calculated from maximum length class in the data),

  • K curving coefficient (default: 1),

  • t_anchor time point anchoring growth curves in year-length coordinate system, corresponds to peak spawning month (range: 0 to 1, default: 1),

  • C amplitude of growth oscillation (range: 0 to 1, default: 1),

  • ts summer point (ts = WP - 0.5) (range: 0 to 1, default: 1);

popSize

the population size. Default: 50

maxiter

the maximum number of iterations to run before the GA search is halted. default:100

run

the number of consecutive generations without any improvement in the best fitness value before the GA is stopped. Default: equals maxiter.

parallel

Whether a logical or integer argument specifying the configuration of parallel computing. See ga for details.

pmutation

the probability of mutation in a parent chromosome. Usually mutation occurs with a small probability, and by default is set to 0.1.

pcrossover

the probability of crossover between pairs of chromosomes. Typically this is a large value and by default is set to 0.8.

elitism

the number of best fitness individuals to survive at each generation. By default the top 5% individuals will survive at each iteration.

MA

number indicating over how many length classes the moving average should be performed (default: 5, for more information see lfqRestructure)

addl.sqrt

additional square root transformation of positive values according to Brey et al. (1988) (default: FALSE, for more information see lfqRestructure)

agemax

maximum age of species; default NULL, then estimated from L_{inf}.

...

additional parameters to pass to ga.

seed

seed value for random number reproducibility.

nresamp

numeric, the number of permutations to run (by default nresamp = 10).

resample

logical. Do you want that lfq object be resampled (TRUE by default).

outfile

character; path of the file which will register the progress of the permutation completions. If it is set as false, NA or NULL, no file will be created.

Details

If resample = TRUE, a full non-parametric bootstrap is performed with resampling from the original length-frequencies by using the function lfqResample. Otherwise, if resample = FALSE, a partial bootstrap is performed, reflecting solution variation due only to the search algorithm, with repeated fitting to the original data (no resampling is performed).

Value

An object of class lfqBoot containing 2 levels:

$bootRaw

A data.frame of fitted VBGF parameters (columns) by resampling (rows).

$seed

A numeric vector of seed values set prior to each resampling call to lfqResample.

References

Examples

# load data
data("alba", package = "TropFishR")

# Define settings (for demo only, fast settings)
MA        <- 7
low_par   <- list(Linf = 9, K = 0.4, t_anchor = 0.5, C = 0, ts = 0)
up_par    <- list(Linf = 11, K = 0.6, t_anchor = 0.8, C = 1, ts = 1)
popSize   <- 12
maxiter   <- 5
run       <- 4
pmutation <- 0.1
nresamp   <-  2

# Non-parallel version
res <- ELEFAN_GA_boot(lfq = alba, MA = MA, seasonalised = FALSE,
                      up_par = up_par, low_par = low_par,
                      parallel = FALSE,
                      popSize = popSize, maxiter = maxiter,
                      run = run, pmutation = pmutation,
                      nresamp = nresamp)

res



# Define settings (for demo only)
MA        <- 7
low_par   <- list(Linf = 8, K = 0.2, t_anchor = 0, C = 0, ts = 0)
up_par    <- list(Linf = 12, K = 0.9, t_anchor = 1, C = 1, ts = 1)
popSize   <- 40
maxiter   <- 30
run       <- 10
pmutation <- 0.2
nresamp   <-  3

# Parallel version
res <- ELEFAN_GA_boot(lfq = alba, MA = MA, seasonalised = FALSE,
                      up_par = up_par, low_par = low_par,
                      parallel = TRUE,
                      popSize = popSize, maxiter = maxiter,
                      run = run, pmutation = pmutation,
                      nresamp = nresamp)

res

Bootstraped ELEFAN_SA

Description

This function performs a bootstrapped fitting of a von Bertalanffy growth function (VBGF) via the ELEFAN_SA function. Most of the arguments are simply passed to the function within many permutations (resampling) of the original lfq data. Partial (repeated fitting on original data) and full bootstrap (with resampling) routines are possible, depending on resample.

Usage

ELEFAN_SA_boot(
  lfq,
  seasonalised = FALSE,
  init_par = list(Linf = 50, K = 0.5, t_anchor = 0.5, C = 0, ts = 0),
  low_par = NULL,
  up_par = NULL,
  SA_time = 60 * 1,
  maxit = NULL,
  nb.stop.improvement = NULL,
  SA_temp = 1e+05,
  MA = 5,
  addl.sqrt = FALSE,
  agemax = NULL,
  seed = NULL,
  nresamp = 10,
  resample = TRUE,
  parallel = FALSE,
  outfile = NA
)

Arguments

lfq

a length frequency object of the class lfq (see lfqCreate).

seasonalised

logical; indicating if the seasonalised von Bertalanffy growth function should be applied (default: FALSE).

init_par

a list providing the Initial values for the components to be optimized. When set to NULL the following default values are used:

  • Linf length infinity in cm (default is the maximum length class in the data),

  • K curving coefficient (default: 0.5),

  • t_anchor time point anchoring growth curves in year-length coordinate system, corresponds to peak spawning month (range: 0 to 1, default: 0.5),

  • C amplitude of growth oscillation (range: 0 to 1, default: 0),

  • ts summer point (ts = WP - 0.5) (range: 0 to 1, default: 0);

low_par

a list providing the lower bounds for components. When set to NULL the following default values are used:

  • Linf length infinity in cm (default is calculated from maximum length class in the data),

  • K curving coefficient (default: 0.01),

  • t_anchor time point anchoring growth curves in year-length coordinate system, corresponds to peak spawning month (range: 0 to 1, default: 0),

  • C amplitude of growth oscillation (range: 0 to 1, default: 0),

  • ts summer point (ts = WP - 0.5) (range: 0 to 1, default: 0);

up_par

a list providing the upper bounds for components. When set to NULL the following default values are used:

  • Linf length infinity in cm (default is calculated from maximum length class in the data),

  • K curving coefficient (default: 0.01),

  • t_anchor time point anchoring growth curves in year-length coordinate system, corresponds to peak spawning month (range: 0 to 1, default: 0),

  • C amplitude of growth oscillation (range: 0 to 1, default: 0),

  • ts summer point (ts = WP - 0.5) (range: 0 to 1, default: 0);

SA_time

numeric; Maximum running time in seconds (default : 60 * 1).

maxit

Integer. Maximum number of iterations of the algorithm. Default is NULL.

nb.stop.improvement

Integer. The program will stop when there is no any improvement in 'nb.stop.improvement' steps. Default is NULL

SA_temp

numeric; Initial value for temperature (default : 1e5).

MA

number indicating over how many length classes the moving average should be performed (defalut: 5, for more information see lfqRestructure).

addl.sqrt

Passed to lfqRestructure. Applied an additional square-root transformation of positive values according to Brey et al. (1988). (default: FALSE, for more information see lfqRestructure).

agemax

maximum age of species; default NULL, then estimated from L_{inf}.

seed

seed value for random number reproducibility.

nresamp

numeric, the number of permutations to run (by default nresamp = 10).

resample

logical. Do you want that lfq object be resampled (TRUE by default).

parallel

Whether a logical or integer argument specifying the configuration of parallel computing. If parallel = TRUE, the number of threads will be defined as parallel::detectCores() - 2.

outfile

character; path of the file which will register the progress of the permutation completions. If it is set as false, NA or NULL, no file will be created.

Details

If resample = TRUE, a full non-parametric bootstrap is performed with resampling from the original length-frequencies by using the function lfqResample. Otherwise, if resample = FALSE, a partial bootstrap is performed, reflecting solution variation due only to the search algorithm, with repeated fitting to the original data (no resampling is performed).

Value

An object of class lfqBoot containing 2 levels:

$bootRaw

A data.frame of fitted VBGF parameters (columns) by resampling (rows).

$seed

A numeric vector of seed values set prior to each resampling call to lfqResample.

References

Examples

# load data
data("alba", package = "TropFishR")

# Define settings (for demo only, fast settings)
MA       <- 7
low_par   <- list(Linf = 9, K = 0.4, t_anchor = 0.5, C = 0, ts = 0)
up_par    <- list(Linf = 11, K = 0.6, t_anchor = 0.8, C = 1, ts = 1)
init_par <- list(Linf = 10, K = 0.5, t_anchor = 0.6, C = 0.5, ts = 0.5)
SA_time  <- 1.5
SA_temp  <- 1e5
nresamp  <- 2

# Non-parallel bootstrapped curve fiting
res <- ELEFAN_SA_boot(lfq = alba, MA = MA, seasonalised = FALSE,
                      init_par = init_par, up_par = up_par, low_par = low_par,
                      SA_time = SA_time, SA_temp = SA_temp,
                      nresamp = nresamp)

res


# Define settings (for demo only)
MA       <- 7
low_par  <- list(Linf = 8, K = 0.1, t_anchor = 0, C = 0, ts = 0)
init_par <- list(Linf = 12, K = 0.5, t_anchor = 0.5, C = 0.5, ts = 0.5)
up_par   <- list(Linf = 15, K = 5, t_anchor = 1, C = 1, ts = 1)
SA_time  <- 10
SA_temp  <- 1e5
nresamp  <- 12

# parallel version
res <- ELEFAN_SA_boot(lfq = alba, MA = MA, seasonalised = FALSE,
                      init_par = init_par, up_par = up_par, low_par = low_par,
                      SA_time = SA_time, SA_temp = SA_temp,
                      nresamp = nresamp,
                      parallel = TRUE)

res

Linf/K scatterplot of bootstrapping results

Description

This function plots a scatterplot of von Bertalanffy growth function parameters K vs L_{inf} (“Kimura plot”) with histograms.

Usage

LinfK_scatterhist(
  res,
  Linf.breaks = "Sturges",
  K.breaks = "Sturges",
  gridsize = rep(151, 2),
  H = NULL,
  shading = TRUE,
  shading.cols = NULL,
  dens.contour = TRUE,
  probs = c(25, 50, 75, 95),
  phi.contour = TRUE,
  phi.levels = NULL,
  phi.contour.col = 8,
  phi.contour.lty = 2,
  phi.contour.lwd = 1,
  phi.contour.labcex = 0.75,
  pt.pch = 16,
  pt.col = adjustcolor(1, 0.25),
  pt.cex = 0.5,
  pt.bg = 4,
  xlab = expression(italic("L")[infinity]),
  ylab = expression(italic("K")),
  ...
)

Arguments

res

Object of class data.frame, tbl_df, lfqBoot or grotagBoot.

Linf.breaks, K.breaks

Arguments passed to hist function to compute the breakpoints (argument breaks) for Linf and K histograms respectively.

gridsize

numeric 2-length vector specifying the resolution of the grid.

H

Plug-in bandwidth object from Hpi (Default: H = ks::Hpi(res[,c("Linf", "K")]))

shading

logical. Do you want to colour 2D field of density estimates? (Default TRUE)

shading.cols

Colors or color palette used for background shading of 2D field of density estimates. No considered if shading = FALSE.

dens.contour

logical. Do you want to add contour lines? (TRUE by default).

probs

numeric Density probability cutoffs (in by contours. By default probs = c(25, 50, 75, 95) and not considered if dens.contour = FALSE.

phi.contour

logical. Do you want to display phi prime isolines? (FALSE by default)

phi.levels

numeric vector that controls Phi prime values. Omitted if phi.contour = FALSE and if NULL values will be chosen automatically by the contour function.

phi.contour.col, phi.contour.lty, phi.contour.lwd, phi.contour.labcex

Extra arguments used to control the color, line type, line width and labels size of the phi prime contour isolines.

pt.pch, pt.col, pt.cex, pt.bg

Extra arguments to control type, color, size and background color of resampling points.

xlab, ylab

Labels for X and Y axis respectively.

...

Extra arguments passed to main plot function.

Details

Isolines of growth performance (Phi’) can be plotted, as well as bivariate 95 used for plotting is usually the result of a bootstrapped growth analysis (i.e. a lfqBoot object generated by fishboot functions such as ELEFAN_SA_boot, ELEFAN_GA_boot, grotag_boot, or grolenage_boot).

If NULL, it will be defined as colorRampPalette(c("white", blues9))(1e3).

Value

This function returns just the described plot.

Examples

data(alba_boot) # lfqBoot object
LinfK_scatterhist(res = alba_boot)

data(bonito_boot) # grotagBoot object
LinfK_scatterhist(res = bonito_boot)

Bootstrapped VBGF estimates for the alba data set

Description

Bootstrapped VBGF estimates for the alba length frequency data set as estimated by ELEFAN_GA.

Usage

alba_boot

Format

alba_boot

A lfqBoot object with two levels:

$bootRaw

A data.frame of fitted VBGF parameters (columns) by resampling (rows).

$seed

A numeric vector of seed values set prior to each resampling call to lfqResample.

Bootstrapped growth estimates for the bonito data set

Description

Bootstrapped growth estimates for the bonito age-growth data calculated using the grotag_boot as this: 

data("bonito", package = "fishmethods")

grotag_boot(input.data = bonito,
            alpha = 35, beta = 55,
            design  = list(nu = 1, m = 1,p = 1, sea = 1),
            stvalue = list(sigma = 0.9, nu = 0.4, m = -1,
                           p = 0.2, u = 0.4, w = 0.4),
            upper   = list(sigma = 5, nu = 1, m = 2,
                           p = 0.5, u = 1, w = 1),
            lower   = list(sigma = 0, nu = 0, m = -2,
                           p = 0.0, u = 0, w = 0),
            control = list(maxit = 1e4),
            seed = 1234, nresamp = 100, cc.only = TRUE)

Usage

bonito_boot

Format

bonito_boot

A grotagBoot object with four columns: Linf, K, PhiL, u, w and seed.

Bootstrapped length-at-age analysis

Description

This function obtains growth parameter estimates from length-at-age data. Since it internally uses the function growth_length_age, this function allows to perform different methods: Gulland and Holt, Ford Walford, Chapman, Bertalanffy, or non linear least squares method (LSM).

This function performs bootstrapped fitting of the von Bertalanffy growth function with estimated growth parameters (L_{inf}, K and t_0) from length-at-age data, based on the function growth_length_age. The output is an object containing the parameters L_{inf}, K and t_0 (named here t_anchor) as well as the growth performance index Phi’ (named PhiL).

Usage

grolenage_boot(
  param,
  method = "LSM",
  Linf_est = NA,
  Linf_init = 100,
  K_init = 0.1,
  t0_init = 0,
  seed = NULL,
  nresamp = 200,
  nan_action = c("nothing", "nanrm", "narm", "force"),
  time_lim = 5 * 60
)

Arguments

param

a list (or data.frame) consisting of following parameters (levels/columns):

  • age: age measurements (e.g. from otoliths),

  • length: corresponding length measurements.

method

indicating which of following methods should be applied: "GullandHolt", "FordWalford", "Chapman", "BertalanffyPlot", or "LSM" (it corresponds to the non-linear least squares fitting method, and is the default, which is recommended for bootstrapping growth).

Linf_est

BertalanffyPlot requires an estimate for L_{inf} to derive K and t_0 (for more information see Details).

Linf_init

initial parameter of L_{inf} for non-linear squares fitting (default 100).

K_init

initial parameter of K for non-linear squares fitting (default 0.1).

t0_init

initial parameter of L_0 for non-linear squares fitting (default 0).

seed

seed value for random number reproducibility (if it NULL by default, it will set internally as seed = as.numeric(Sys.time())).

nresamp

numeric; the number of permutations to run (Default: nresamp = 200).

nan_action

character that defines the action that the function will execute if there is a row with NaN (growth rate parameters inestimable for that resample):

  • nothing: the function will return the results including the NaNs (default).

  • nanrm or narm: after having the results, it will only returns the rows without NaNs. For this case narm and nanrm are equivalent, but it should be noted that the function will look for and omit the NaNs (and not the NAs). See Details.

  • force: The function will start an iterative process changing the internal seed values until it fulfills the nresamp. It only works together with the time_lim argument. See Details.

time_lim

If nan_action = "force", it defines the maximum time (in seconds) that the function will last resampling until it achieves a result output with no-NaN rows.

Details

It is important to take into account the particular considerations of each method regarding the required parameters, so it is recommended to read the Details of the documentation of growth_length_age.

CI and plotting arguments (of growth_length_age) are set as FALSE for each bootstrap call here. By default, growth_length_age generates a plot when it is called, so internally grolenage_boot executes a dev.off call in order to prevent it.

nan_action = "force" should be used carefully, as estimated NaN VBGF parameter values are not always a result of bootstrap data selection factors. Few resamples should first be tested with different Linf_init, K_init and t0_init values. No selection of the realistic initial parameters may also result in NaN values being obtained. The search time may depend on the size of the input set. For example, if you have many thousands of individuals or if (in addition) the value of nresamp is too high, it is possible that the function will take a long time before obtaining complete results. Even though time_lim avoids falling into an infinite loop by limiting the time used by this process to 5 minutes, this value is referential and might be insufficient due to the factors mentioned above.

t_anchor is the true t_0 estimate in the case of true length-at-age data, but it will only be available from "BertalanffyPlot" or "LSM" methods. For the other methods, a vector of NAs will be returned instead.

Value

An object of class lfqBoot containing 2 levels:

$bootRaw

A data.frame of fitted VBGF parameters (columns) by resampling (rows).

$seed

A numeric vector of seed values set prior to each resampling call.

References

Examples

# Synthetical length at age data
dat <- list(age = rep(x = 1:7,each = 15),
            length = c(rnorm(n = 15, mean = 4.6, sd = 4),
                       rnorm(n = 15, mean = 22.8, sd = 7),
                       rnorm(n = 15, mean = 35, sd = 7),
                       rnorm(n = 15, mean = 43, sd = 7),
                       rnorm(n = 15, mean = 49, sd = 5),
                       rnorm(n = 15, mean = 53, sd = 5),
                       rnorm(n = 15, mean = 57, sd = 3)))

# Perform bootstrapped curve fitting with grolenage_boot
res <- grolenage_boot(param = dat, nresamp = 70)

# Plot scatter histograms of Linf and K
LinfK_scatterhist(res = res)

# Plot univariate density plots of all parameters
univariate_density(res = res)

# Plot swarm plots of all n bootstraps
vbgfCI_time(res = res)

# Extract data.frame with all parameter estimates
# for comparisons of posterior distributions
print(res$bootRaw)

Bootstrapped tag-and-recapture growth analysis

Description

This function performs bootstrapped fitting of the von Bertalanffy growth function (VBGF) with estimated growth parameters (L_{inf}, K and t_0) from tag-and-recapture data, based on the function grotag, that estimates VBGF parameters according to Francis (1988). The output is an object containing the parameters L_{inf} and K, as well as the growth performance index Phi’ (named PhiL).

This function resamples the input.data data by rows (i.e., by recapture date) several times (nresamp times, default: nresamp = 200). Then, a VBGF curve is fitted to each resampled data set. The output (a list of class lfqBoot) will store results (e.g., VGBGF function parameters K and Linf) in a data.frame accessible through $bootRaw. The $bootRaw table also includes the growth performance index Phi', seasonal parameters u and w (sensu Francis, 1988), which are equal to C (sensu Pauly and Gaschütz, 1979) and and ts (sensu Mildenberger et al., 2017). The $bootRaw table also includes seed values and system time.

Usage

grotag_boot(
  L1 = NULL,
  L2 = NULL,
  T1 = NULL,
  T2 = NULL,
  alpha = NULL,
  beta = NULL,
  design = list(nu = 0, m = 0, p = 0, sea = 0),
  stvalue = list(sigma = 0.9, nu = 0.4, m = -1, p = 0.1, u = 0.4, w = 0.4),
  upper = list(sigma = 5, nu = 1, m = 2, p = 1, u = 1, w = 1),
  lower = list(sigma = 0, nu = 0, m = -2, p = 0, u = 0, w = 0),
  gestimate = TRUE,
  st.ga = NULL,
  st.gb = NULL,
  st.galow = NULL,
  st.gaup = NULL,
  st.gblow = NULL,
  st.gbup = NULL,
  control = list(maxit = 10000),
  input.data = NULL,
  seed = NULL,
  nresamp = 200,
  na_action = c("nothing", "narm", "force"),
  time_lim = 5 * 60
)

Arguments

L1, L2, T1, T2

Name of the columns to be extracted from input.data and used by the grotag function for the arguments L1, L2, T1 and T2, respectively. See Details.

alpha

numeric value giving an arbitrary length alpha.

beta

numeric value giving an arbitrary length beta (beta > alpha).

design

list specifying the design of the model to estimate. Use 1 to designate whether a parameter(s) should be estimated. Type of parameters are:

  • nu: growth variability (1 parameter).

  • m: bias parameter of measurement error (1 parameter).

  • p: outlier probability (1 parameter).

  • sea: seasonal variation (2 parameters: u and w).

Model 1 of Francis is the default settings of 0 for nu, m, p and sea.

stvalue

Starting values of sigma(s) and depending on the design argument, nu, m, p, u, and w used as input in the nonlinear estimation (function optim) routine.

upper, lower

Upper and lower limits of the model parameters' (nu, m, p, u, and w) region to be investigated.

gestimate

logical specifying whether starting values of ga and gb (growth increments of alpha and beta) should be estimated automatically. TRUE by default.

st.ga, st.gb

If gestimate=FALSE, user-specified starting value for ga and gb respectively.

st.galow, st.gaup

If gestimate=FALSE, user-specified lower and upper limits for st.ga used in optimization.

st.gblow, st.gbup

If gestimate=FALSE, user-specified lower and upper limits for st.gb used in optimization.

control

Additional controls passed to the optimization function optim.

input.data

A growth increment object of the class data.frame.

seed

seed value for random number reproducibility (if it NULL by default, it will set internally as seed = as.numeric(Sys.time())).

nresamp

numeric; the number of permutations to run (Default: nresamp = 200).

na_action

character that defines the action that the function will execute if there is a row with NA:

  • nothing: the function will return the results including the NAs (default).

  • narm: after having the results, it will only returns the rows without NAs. See Details.

  • force: The function will start an iterative process changing the internal seed values until it fulfills the nresamp. It works just together time_lim argument. See Details.

time_lim

If na_action = "force", it defines the maximum time (in seconds) that the function will last resampling until it achieves a result output with no-NaN rows.

Details

There are 2 ways to specify the main input arguments (related to the size and timing of the mark-recapture): (1) in the classical way, i.e. by defining L1, L2, T1 and T2 as numeric vectors as indicated in the grotag documentation or (2) through a data.frame indicated in the input.data argument. In the latter case, the arguments L1, L2, T1 and T2 must be 1-length character vectors and they will serve to indicate the column names of the corresponding variables. If only one value is specified for input.data and any of the other arguments is NULL, a default name equal to the variable name will be assigned (e.g. L1 <- “L1”).

na_action = "force" should be used carefully, as it is not always due to bootstrap data selection factors, but also to an inadequate selection of the estimation parameters that the NA values are obtained. Also, the search time may depend on the size of the input set, if you have many thousands of individuals or if (in addition) the value of nresamp is high, it is possible that the function will take a long time before obtaining complete results. time_lim avoids falling into an infinite loop by limiting the time used by this process to 5 minutes, but this value is referential and may be insufficient due to the factors mentioned above.

Value

A data.frame of fitted VBGF parameters (columns) by resampling (rows). It includes a column (seed) with seed values set prior to each resampling call.

References

Examples

# Load example DB from fishmethods package
data(bonito, package = "fishmethods")

## Run the example cited on ?grotag
 #  fishmethods::grotag(L1 = bonito$L1,
 #                   L2 = bonito$L2,
 #                   T1 = bonito$T1,
 #                   T2 = bonito$T2,
 #                   alpha   = 35, beta = 55,
 #                   design  = list(nu = 1, m = 1,p = 1, sea = 1),
 #                   stvalue = list(sigma = 0.9, nu = 0.4, m = -1, p = 0.2, u = 0.4, w = 0.4),
 #                   upper   = list(sigma = 5, nu = 1, m = 2, p = 0.5, u = 1, w = 1),
 #                   lower   = list(sigma = 0, nu = 0, m = -2, p = 0.0, u = 0, w = 0),
 #                   control = list(maxit = 1e4))

# Run the example using grotag_boot
res <- grotag_boot(L1 = bonito$L1,
                   L2 = bonito$L2,
                   T1 = bonito$T1,
                   T2 = bonito$T2,
                   alpha   = 35, beta = 55,
                   design  = list(nu = 1, m = 1,p = 1, sea = 1),
                   stvalue = list(sigma = 0.9, nu = 0.4, m = -1, p = 0.2, u = 0.4, w = 0.4),
                   upper   = list(sigma = 5, nu = 1, m = 2, p = 0.5, u = 1, w = 1),
                   lower   = list(sigma = 0, nu = 0, m = -2, p = 0.0, u = 0, w = 0),
                   control = list(maxit = 1e4), seed = 18,
                   nresamp = 3, na_action = "nothing")

res

Resampling of length-frequency data

Description

This function resamples the lfq data by sampling dates. Sampling is done in a non-parametric way following the relative frequencies of the original data, allowing for individual counts to be selected more than once (i.e. replace = TRUE in sample), and resulting in total counts (by sample) equal to the original data.

Usage

lfqResample(lfq)

Arguments

lfq

A length frequency object of the class lfq.

Value

A resampled version of the lfq class dataset.

Examples

# Load data
data("alba", package = "TropFishR")

# Resample lfq data
alba_p <- lfqResample(lfq = alba)

# Side-by-side plot
op <- par(no.readonly = TRUE)
par(mfcol = c(2, 1), mar = c(4, 4, 2, 1))

# Original
plot(x = TropFishR::lfqRestructure(alba), Fname = "rcounts")
mtext("original", side=3, line=0.25)

# Resampled
plot(TropFishR::lfqRestructure(alba_p), Fname = "rcounts")
mtext("resampled", side=3, line=0.25)

par(op)

Univariate kernel density estimate plot of VBGF parameter from bootstrapping results

Description

This function plots a set of vertical plots with kernel density distributions for univariate posterior distributions of the VBGF growth parameters L_{inf}, K, and Phi’. The 95 interval and the most likely optimum fit estimate are shown for each parameter.

Usage

univariate_density(
  res,
  CI = 95,
  use_hist = FALSE,
  nbreaks = 10,
  mar = c(1.5, 2, 2, 0),
  oma = c(1.5, 0, 0, 0.5),
  mgp = c(2, 0.5, 0),
  tcl = -0.25,
  cex = 1,
  ...
)

Arguments

res

Object of class lfqBoot.

CI

numeric. Confidence interval in % (default: 95).

use_hist

logical Plot histogram in addition to smoothed kernel density.

nbreaks

numeric vector specifying the number of breaks in the histogram.

mar, oma, mgp, tcl, cex, ...

Additional arguments passed to par.

Details

This function used the function kde to obtain kernel density estimates for the VBGF growth parameters L_{inf}, K, and Phi’. The 95 posterior distribution) and the most likely optimum fit estimate (i.e., the mode of each posterior distribution) are then plotted inside each vertical plot. The input used for plotting is usually the result of a bootstrapped growth analysis (i.e. a lfqBoot object generated by fishboot functions such as ELEFAN_SA_boot, ELEFAN_GA_boot, grotag_boot, or grolenage_boot).

Value

This function returns just the described plot.

Examples

data(alba_boot)
univariate_density(alba_boot)

VBGF plot and CI

Description

This function plots a swarm of von Bertalanffy growth functions (VBGF), as length vs time (age) curves, based on the results of bootstrap runs, with confidence intervals (CI).

Usage

vbgfCI_time(
  res,
  CI = 95,
  agemax = NULL,
  plot = TRUE,
  add_legend = TRUE,
  add_max_dens_legend = TRUE,
  xlab = "Relative time",
  ylab = "Length",
  perm.col = adjustcolor("grey50", 0.1),
  perm.lwd = 1,
  ci.col = "black",
  ci.lty = 2,
  ci.lwd = 1,
  maxd.col = "black",
  maxd.lty = 1,
  maxd.lwd = 2,
  ...
)

Arguments

res

Object with L_{inf}, K and t_0, it could be a data.frame, a tbl_df, a list, grotagBoot or a lfqBoot object. See Details.

CI

numeric. Confidence interval in % (default: 95).

agemax

numeric values indicating the maximum number of years to project.

plot

logical. If TRUE (default), a plot is returned, otherwise just a list with levels limCI, inCI, density and max_dens. See Value for a detailed description of each one.

add_legend

logical. Should CI and max. density legend be added (Default: 'add_legend = TRUE').

add_max_dens_legend

logical. Should maximum density line be added (Default: 'add_max_dens_legend = TRUE').

xlab

Label for x-axis

ylab

Label for y-axis

perm.col, perm.lwd

Color and width for each resample estimate line.

ci.col, ci.lty, ci.lwd

Color, type and width for CI line.

maxd.col, maxd.lty, maxd.lwd

Color, type and width for maximum density line.

...

Extra arguments passed to the main plot function.

Details

Each thin grey line represents the output of a single bootstrap run. The most likely optimum (i.e., the mode of the posterior distribution) is shown as a thick black line. The dashed lines show the upper and lower limits of the 95 confidence envelope. The input used for plotting is usually the result of a bootstrapped growth analysis (i.e. a lfqBoot object generated by fishboot functions such as ELEFAN_SA_boot, ELEFAN_GA_boot, grotag_boot, or grolenage_boot).

Value

A list containing:

$limCI

A data.frame with CI limits by time.

$inCI

A data.frame with logical values defining whether bootstrapping samples are within each of the defined CIs.

$density

The multivariate kernel density estimates for each sample.

$max_dens

A list with the VBGF parameter combination having the maximum density estimate.

Examples

data(alba_boot) # lfqBoot object
vbgfCI_time(res = alba_boot)

vbgfCI_time(res = alba_boot, CI = c(50, 95),
            ci.col = c("red", "orange"))

data(bonito_boot) # grotagBoot object
LinfK_scatterhist(res = bonito_boot)