R/datasets.r
In haddonm/MQMF: Modelling and Quantitative Methods in Fisheries
#' @title abdat A list of fishery data for blacklip abalone
#'
#' @description A dataset of fishery data for blacklip abalone
#' (\emph{Haliotis rubra}) from part of the Tasmanian west coast
#' for the years 1985 - 2008. It contains a data.frame containing
#' the year, the catch, and the standardized CPUE from four
#' statistical blocks of Tasmania's west coast combined. In
#' particular, it can be used when fitting a surplus production
#' model. Workable initial parameter values, before log-transformation
#' might be: r= 0.4, K=9400, Binit=3400, sigma=0.05 for the
#' Schaefer version, while these also work for the Fox model
#' one could more efficiently use r=0.3, K=12000, Binit=4000, sigma=0.05.
#'
#' @name abdat
#'
#' @docType data
#'
#' @format A data.frame of three columns
#' \describe{
#' \item{year}{the annual year in which the catches occurred}
#' \item{catch}{the reported landed catch in tonnes, to the nearest kilogram}
#' \item{cpue}{the standardized catch-per-unit-effort for this dive fishery}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Surplus Production Modelling, Schaefer and Fox models
#' \item Model fitting using maximum likelihood
#' \item Uncertainty examples
#' }
#'
#' @source Catch data from Mundy, C. and J. McAllister (2019) Tasmanian abalone fishery assessment 2018, Institute for Marine and Antarctic Studies, University of Tasmania, 190p. ISBN: 978-1-925646-46-7. The cpue data is an unpublished early attempt at standardizing the cpue data with respect to month, block, and diver. Many more details are now included in such analyses.
#'
#' @examples
#' data(abdat)
#' print(abdat)
#' oldpar <- par(no.readonly=TRUE)
#' plot(abdat$year,abdat$cpue, type="l",xlab="year",ylab="CPUE",
#' panel.first=grid())
#' points(abdat$year,abdat$cpue,pch=16,cex=1.2)
#' par(oldpar)
NULL
#' @title blackisland tagging data from a blacklip abalone population
#'
#' @description A 108 x 4 data.frame containing dt, the time
#' in years between tagging and recapture, l1 the shell length
#' at tagging, and l2, the length at recapture, with the growth
#' increment, dl as the last column. This data can be used to
#' estimate the growth characteristics of
#' abalone from the Black Island site, which is on the south west
#' coast of Tasmania, Australia. The mean time
#' interval between tagging and recapture is 1 year and 1 week,
#' 1.02 years, which reflects the practical problems of taking a
#' vessel around the bottom of Tasmania, where it is essential to
#' wait on suitable weather for such sub-tidal field work.
#'
#' @name blackisland
#'
#' @docType data
#'
#' @format A data.frame of four columns
#' \describe{
#' \item{dt}{the time between tagging and recapture, in years}
#' \item{l1}{the shell length when tagged in mm}
#' \item{l2}{the shell length at recapture in mm}
#' \item{dl}{the growth increment between tagging and recapture
#' in mm; there are zero values.}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Tagging data
#' \item Estimation of individual growth
#' \item Binomial likelihoods
#' \item Faben's version of the von Bertalanffy curve
#' }
#'
#' @source Thanks to Dr Craig Mundy and the abalone team at the Institute of
#' Marine and Antarctic Studies, of the University of Tasmania for the use
#' of this data.
#'
#' @examples
#' data(blackisland)
#' print(head(blackisland,20))
#' oldpar <- par(no.readonly=TRUE)
#' plot(blackisland$l1,blackisland$dl,type="p",pch=16,
#' xlab="Initial Length mm",ylab="Growth Increment mm",
#' panel.first=grid())
#' abline(h=0)
#' par(oldpar)
NULL
#' @title dataspm A data.frame of fisheries catch and cpue data
#'
#' @description A data.frame containing 31 years of catch, standardized
#' cpue, number of records, and the unstandardized geometric mean
#' cpue for Pink Ling (\emph{Genypterus blacodes}). The fisheries data can
#' be used in the surplus production modelling in Chapter 7. Initial
#' parameter estimates very close to the optimum values could be
#' param <- log(c(r=0.25, K=5500, Binit=3000,sigma=0.2)) for the Schaefer
#' model and log(c(r=0.15, K=6500, Binit=3000, sigma=0.2)) for the Fox model
#'
#' @name dataspm
#'
#' @docType data
#'
#' @format A 31 x 5 data.frame
#' \describe{
#' \item{year}{the year from 1986 t0 2016}
#' \item{catch}{the catch in tonnes to the nearest 100kg}
#' \item{cpue}{the standardized cpue scaled to the mean of the series}
#' \item{records}{the number of records making up the yearly totals}
#' \item{geom}{the naive geometric mean cpue of the raw data as kg/hr, also
#' rescaled to the mean of the series}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Fishery data-set
#' \item Surplus Production Modelling
#' \item Log-Normal likelihoods
#' }
#'
#' @source Haddon, M. and M. Sporcic (2017) Catch rate standardizations for
#' selected SESSF Species (data to 2016) pp 43-406 in Tuck, G.N.
#' (\emph{ed}) \emph{Stock Assessment for the Southern and Eastern scalefish
#' and shark fishery 2016 and 2017.} 837 pp. ISBN 978-1-4863-1012-8 data
#' extracted from Table 7.96 PinkLing4050 page 216.
#'
#' @examples
#' data(dataspm)
#' oldpar <- par(no.readonly=TRUE)
#' plot(dataspm$year,dataspm$geom,type="l",lwd=2,xlab="Year",
#' ylab="CPUE",panel.first=grid())
#' lines(dataspm$year,dataspm$cpue*mean(dataspm$geom),lwd=2,col=2)
#' legend("topright",c("cpue","geom"), col=c(1,2), lwd=3, bty="n",
#' cex=1.2)
#' par(oldpar)
NULL
#' @title LatA Simulated length-at-age for 358 female fish
#'
#' @description A data.frame containing the simulated
#' age for an array of different lengths based upon the properties
#' of an extensive collection of redfish (\emph{Centroberyx affinis})
#' length-at-age data from eastern Australia sampled in the 1990's.
#'
#' @name LatA
#'
#' @docType data
#'
#' @format A data.frame with 358 rows and 2 variables:
#' \describe{
#' \item{age}{simulated ages in years}
#' \item{length}{consequent simulated fork length of the fish, in cms}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Estimating individual growth from length-at-age data
#' \item von Bertalanffy growth curve
#' \item Gompertz growth curve
#' \item Michaelis-Menton curve used as a growth curve
#' }
#'
#' @source The data this simulation is based upon is from length-at-age
#' data for one species collected over many years by the many
#' excellent people running the Integrated Stock Monitoring Program
#' in the Australian South East Fishery over the years of its
#' existence. The simulation is based on a characterization of
#' redfish properties and includes random error in the hypothetical
#' measurements as well as the processes of growth (i.e. both
#' measurement and process error). The other inputs were a selected
#' set of growth parameters and the relative frequency of different
#' ages vs lengths.
#'
#' @examples
#' data(LatA)
#' pars <- c(27.0,0.15,-2.0) # von Bertalanffy
#' bestvB <- nlm(f=ssq,funk=vB,observed=LatA$length,p=pars,
#' ages=LatA$age,typsize=magnitude(pars))
#' outfit(bestvB,backtran=FALSE,title="vB")
NULL
#' @title minnow contains weekly growth data for use with seasonal growth curves
#'
#' @description minnow is a dataset of mean length against time in weeks for
#' minnows (\emph{Phoxinus phoxinus}), derived from Pitcher &
#' Macdonald (1973) for use when fitting growth curves, especially
#' seasonal growth curves. The data exhibit increases and decreases
#' in length as each year progresses because these are mean lengths
#' rather than individual measurements (which would, more typically,
#' be used these days). The data have been read off a graph within
#' the original paper as it is not reported explicitly, and are
#' therefore only approximate, but will do for our purposes (but
#' expect different parameters to those reported in the original
#' paper). This is length at time not age. Though time is being
#' used as a proxy for age there is no age 0.
#'
#' @name minnow
#'
#' @docType data
#'
#' @format A data.frame of mean length-at-time data
#' \describe{
#' \item{week}{the week of sampling minnows for lengths}
#' \item{length}{the estimated mean length in the corresponding week in mm}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item seasonal growth curves
#' \item von Bertalanffy
#' \item Model residuals
#' }
#'
#' @source data measured from Figure 2, page 602 in Pitcher, T.J., and P.D M. MacDonald. (1973)
#' Two models of seasonal growth. \emph{Journal of Applied Ecology} 10:599–606.
#'
#' @examples
#' data(minnow)
#' oldpar <- par(no.readonly=TRUE)
#' plot1(minnow$week,minnow$length,type="p",pch=16,cex=1.2,
#' xlab="Week",ylab="Length mm")
#' par(oldpar)
NULL
#' @title npf fishery catch data from Northern Prawn Fishery 1970-1992
#'
#' @description npf is fishery catch data from Australia's Northern
#' Prawn Fishery from 1970 to 1992 summarized from Robins and
#' Somers, 1994. It contains the catches, in tonnes,
#' of banana prawns (\emph{Penaeus merguiensis} and \emph{P. indicus}),
#' tiger prawns (brown - \emph{P. esculentus}) and (grooved - \emph{P.
#' semisulcatus}), endeavour prawns, (\emph{Metapenaeus endeavouri} and
#' \emph{M. ensis}), king prawns (\emph{P. latisulcatus} and
#' \emph{P. longistylus}), the number of vessels fishing, and
#' the annual effort as boat-days.
#'
#' @name npf
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1970 - 1992.}
#' \item{banana}{banana prawn catches, tonnes.}
#' \item{tiger}{tiger prawn catches, tonnes.}
#' \item{endevaour}{endeavour prawn catches, tonnes.}
#' \item{king}{king prawn catches, tonnes.}
#' \item{boats}{the number of vessels fishing in that year.}
#' \item{boatday}{the total annual effort as boatdays.}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item correlation analysis and regression
#' \item Bootstrap percentile confidence intervals
#' \item Model residuals
#' }
#'
#' @source Robins, C. and I. Somers (1994) Appendix A. Fishery
#' Statistics pp 141 - 164 in Pownall, P.C. (ed.) Australia's
#' Northern Prawn Fishery: The first 25 years. NPF25. Cleveland,
#' Australia. 179p.
#' @examples
#' data(npf)
#' npf
#' oldpar <- par(no.readonly=TRUE)
#' plot1(npf$year,npf$tiger,xlab="Year",ylab="Tonnes",lwd=2)
#' lines(npf$year,npf$endeavour,col=2,lwd=2)
#' legend("topleft",c("Tiger","Endeavour"),col=c(1,2),lwd=3,
#' bty="n",cex=1.5)
#' par(oldpar)
NULL
#' @title pttuna is yellowfin tuna fishery data from Pella-Tomlinson 1969
#'
#' @description pttuna is yellowfin tuna fishery data from Pella-Tomlinson's
#' (1969) classical paper describing their generalized surplus production
#' model. This is the same data as contained in the schaef data-set, except
#' it is extended from 1934 - 1967. Some of the values are slightly
#' different, and their table rounds off the cpue estimates slightly
#' differently but the catch and effort figures are theirs. It contains
#' the year, the catch, the effort, and the cpue (which is just the total
#' catch divided by the total effort, a ratio estimate). Initial parameter
#' estimates close to the optimum values for the Schaefer model could be
#' param <- log(c(r=0.28,K=2100000,Binit=2400000,sigma=0.16)). With this
#' longer time-series the eventual MSY estimate was somewhat larger than
#' when just the schaef data are used.
#'
#' @name pttuna
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1934 - 1967}
#' \item{catch}{the total annual catch, '000s of pounds }
#' \item{effort}{the total effort as standard class 4 baitboat fishing days}
#' \item{cpue}{the catch '000 pounds per standard class 4 day, a ratio cpue}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item surplus production modelling
#' \item classical fisheries data
#' \item Log-Normal likelihoods
#' }
#'
#' @source from Table 6 page 457 in Pella, J.J. and P.K. Tomlinson (1969) A
#' Generalized Stock Production Model. \emph{Bulletin, Inter-American
#' Tropical Tuna Commission} 13(3): 421-458. Obtainable from
#' \emph{https://www.iattc.org/BulletinsENG.htm}
#'
#' @examples
#' data(pttuna)
#' pars <- log(c(r=0.25,K=2.1e06,Binit=2.2e06,sigma=0.2))
#' answer <- fitSPM(pars,pttuna,schaefer=TRUE,maxiter=1000)
#' outfit(answer,title="Pella-Tomlinson Data",digits=4)
NULL
#' @title schaef is yellowfin tuna fishery data from Schaefer 1957
#'
#' @description schaef is yellowfin tuna fishery data from Schaefer
#' (1957) It contains the year, the catch, the effort, and the cpue
#' and was used in one of the first descriptions of a stock
#' assessment that used a surplus production model. The catch-per-
#' unit-effort, cpue, is a ratio cpue of the total catch divided by
#' the total effort as thousands of pounds per day. These days such
#' ratios tend not to be used, with individual records
#' for each day's effort being used instead. Using individual records
#' does not obscure the variation between different
#' vessels, areas, depths, and seasons. Initial parameter estimates
#' close to the optimum values for both the Schaefer model and the
#' Fox model could be
#' param <- log(c(r=0.24,K=2100000,Binit=2200000,sigma=0.2))
#'
#' @name schaef
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1934 - 1955}
#' \item{catch}{the total annual catch, '000s of pounds }
#' \item{effort}{the total effort as standard class 4 clipper fishing days}
#' \item{cpue}{the catch '000 pounds per standard class 4 day, a ratio cpue}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item surplus production modelling
#' \item classical fisheries data
#' \item Log-Normal likelihoods
#' }
#'
#' @source from Table 1 page 266 in Schaefer, M.B. (1957) A study of the dynamics of the
#' fishery for yellowfin tuna in the Eastern Tropical Pacific Ocean.
#' Bulletin, Inter-American Tropical Tuna Commission 2: 247-285. Obtainable from
#' \emph{https://www.iattc.org/BulletinsENG.htm}
#'
#' @examples
#' data(schaef)
#' pars <- log(c(r=0.2,K=2.1e06,Binit=2.2e06,sigma=0.2))
#' answer <- fitSPM(pars,schaef,schaefer=TRUE,maxiter=1000)
#' outfit(answer,title="Schaefer, 1957 Data",digits=4)
NULL
#' @title tasab is a matrix of abalone maturity-at-length data
#'
#' @description tasab is a 715 x 4 matrix of maturity-at-length data
#' for blacklip abalone (\emph{Haliotis rubra}) from two sites
#' along the Tasmanian west coast. All data was collected in
#' February 1995, but details, such as site name, accurate
#' location, statistical block, year, month, and other
#' details have been omitted for brevity. See section on maturity
#' within the Static Models chapter for detailed use of this
#' data-set.
#'
#' @name tasab
#'
#' @docType data
#'
#' @format A data.frame of maturity-at-length data
#' \describe{
#' \item{site}{an identifier for the two different sites sampled}
#' \item{sex}{I = immature, M = male, F = female}
#' \item{length}{the shell length in mm}
#' \item{mature}{was the animal mature = 1 or not = 0}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item maturity ogives or logistic curves
#' \item Binomial likelihoods
#' }
#'
#' @source Many thanks to the Institute of Marine and Antarctic Science,
#' which is part of the University of Tasmania, and especially to
#' Dr Craig Mundy, leader of the Abalone Group, for permission to use
#' this data collected in February 1995.
#'
#' @examples
#' data(tasab)
#' head(tasab,20)
#' table(tasab$site,tasab$sex)
NULL
#' @title tigers is tiger prawn recruitment data from Penn & Caputi 1986
#'
#' @description tigers is a dataset of only 14 rows of data with a
#' column of Spawning index and Recruitment index, as a data.frame.
#' The timing of the recruitment index is up to half a year after
#' the spawning index.
#'
#' @name tigers
#'
#' @docType data
#'
#' @format A data.frame of spawning recruitment data
#' \describe{
#' \item{Spawn}{the estimated spawning biomass index in a year (Aug - Oct)}
#' \item{Recruit}{estimated recruitment from the biomass in each year (Mar - May)}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Stock-recruitment curves
#' \item Beverton-Holt and Ricker Models
#' \item Static model fitting
#' }
#'
#' @source Extracted from Table 2, page 496 of Penn, J.W. and N. Caputi
#' (1986) Spawning stock-recruitment relationships and environmental
#' influences on the tiger prawn (\emph{Penaeus esculentus}) fishery
#' in Exmouth Gulf, Western Australia. \emph{Australian Journal of
#' Marine and Freshwater Research} 37: 491-505. Sorted on spawning index.
#'
#' @examples
#' data(tigers)
#' tigers
#' oldpar <- par(no.readonly=TRUE)
#' plot1(tigers$Spawn,tigers$Recruit,type="p",pch=16,cex=1.25)
#' par(oldpar)
NULL
#' @title twoindex has orange roughy catches with hypothetical cpue
#'
#' @description twoindex is a 35 x 4 data.frame of fishery data made
#' up of smoothed real catches but two simulated indices of relative
#' abundance. This data-set is designed to be used to illustrate
#' the implementation of surplus production models when there are
#' more than one time-series of relative abundance indices. This is not
#' currently implemented in the book but is put here for use by readers
#' should they wish to pursue this approach. The
#' indices have been designed to generate a workable answer but also
#' require the use of a penalty on harvest rates to avoid massively
#' inflated harvest rates well above 1. Instead of using simpspm,
#' spm, and negLL1, we need to use simpspmM, spmCE, and negLLM.
#' The cpue series are hypothetical and have been designed to
#' illustrate the use of penalty1 and the use of multiple indices
#' of relative abundance. The real stock assessment uses acoustic
#' survey indices and also uses many years of age composition data
#' inside Stock Synthesis 3, not surprisingly the inclusion of real
#' time-series of indices and of age-composition data leads to very
#' different results.
#'
#' @name twoindex
#'
#' @docType data
#'
#' @format A data.frame of fishery data
#' \describe{
#' \item{year}{the calender year of fishing}
#' \item{catch}{the reported catch in tonnes}
#' \item{cpue1}{the first index of relative abundance}
#' \item{cpue2}{the second index of relative abundance}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Surplus production models
#' \item Dynamic model fitting
#' \item -ve log-likelihoods
#' }
#'
#' @source Catches extracted from Table 4, page 11 of Haddon, M. (2017)
#' Orange Roughy East (Hoplostethus atlanticus) stock assessment
#' using data to 2016 Report to November 2017 SE RAG meeting. CSIRO,
#' Oceans and Atmosphere, Australia. 51p. from
#' https://www.afma.gov.au/fisheries-management/species/orange-roughy
#' Catch data extended to 2019 using AFMA's catchwatch system.
#'
#' @examples
#' data(twoindex)
#' fish <- as.matrix(twoindex)
#' pars <- log(c(0.04,155000,0.4,0.3))
#' bestSP <- nlm(f=negLLM,p=pars,funk=simpspmM,indat=fish,
#' schaefer=TRUE,logobs=log(fish[,c("cpue1","cpue2")]),
#' steptol=1e-06,harvpen=TRUE)
#' namepar <- c("r", "K", "Binit","sigma")
#' outfit(bestSP,parnames=namepar) # best fitting estimates
#' # if 'Either ~local min or steptol too small try 'steptol=1e-05'
#' # plotprep(width=7,height=5,newdev=FALSE) # for external plot
#' answer <- plotspmmod(bestSP$estimate,indat=fish,
#' plotprod=TRUE,maxy=3.4)
NULL
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#' @title abdat A list of fishery data for blacklip abalone
#'
#' @description A dataset of fishery data for blacklip abalone
#' (\emph{Haliotis rubra}) from part of the Tasmanian west coast
#' for the years 1985 - 2008. It contains a data.frame containing
#' the year, the catch, and the standardized CPUE from four
#' statistical blocks of Tasmania's west coast combined. In
#' particular, it can be used when fitting a surplus production
#' model. Workable initial parameter values, before log-transformation
#' might be: r= 0.4, K=9400, Binit=3400, sigma=0.05 for the
#' Schaefer version, while these also work for the Fox model
#' one could more efficiently use r=0.3, K=12000, Binit=4000, sigma=0.05.
#'
#' @name abdat
#'
#' @docType data
#'
#' @format A data.frame of three columns
#' \describe{
#' \item{year}{the annual year in which the catches occurred}
#' \item{catch}{the reported landed catch in tonnes, to the nearest kilogram}
#' \item{cpue}{the standardized catch-per-unit-effort for this dive fishery}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Surplus Production Modelling, Schaefer and Fox models
#' \item Model fitting using maximum likelihood
#' \item Uncertainty examples
#' }
#'
#' @source Catch data from Mundy, C. and J. McAllister (2019) Tasmanian abalone fishery assessment 2018, Institute for Marine and Antarctic Studies, University of Tasmania, 190p. ISBN: 978-1-925646-46-7. The cpue data is an unpublished early attempt at standardizing the cpue data with respect to month, block, and diver. Many more details are now included in such analyses.
#'
#' @examples
#' data(abdat)
#' print(abdat)
#' oldpar <- par(no.readonly=TRUE)
#' plot(abdat$year,abdat$cpue, type="l",xlab="year",ylab="CPUE",
#' panel.first=grid())
#' points(abdat$year,abdat$cpue,pch=16,cex=1.2)
#' par(oldpar)
NULL
#' @title blackisland tagging data from a blacklip abalone population
#'
#' @description A 108 x 4 data.frame containing dt, the time
#' in years between tagging and recapture, l1 the shell length
#' at tagging, and l2, the length at recapture, with the growth
#' increment, dl as the last column. This data can be used to
#' estimate the growth characteristics of
#' abalone from the Black Island site, which is on the south west
#' coast of Tasmania, Australia. The mean time
#' interval between tagging and recapture is 1 year and 1 week,
#' 1.02 years, which reflects the practical problems of taking a
#' vessel around the bottom of Tasmania, where it is essential to
#' wait on suitable weather for such sub-tidal field work.
#'
#' @name blackisland
#'
#' @docType data
#'
#' @format A data.frame of four columns
#' \describe{
#' \item{dt}{the time between tagging and recapture, in years}
#' \item{l1}{the shell length when tagged in mm}
#' \item{l2}{the shell length at recapture in mm}
#' \item{dl}{the growth increment between tagging and recapture
#' in mm; there are zero values.}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Tagging data
#' \item Estimation of individual growth
#' \item Binomial likelihoods
#' \item Faben's version of the von Bertalanffy curve
#' }
#'
#' @source Thanks to Dr Craig Mundy and the abalone team at the Institute of
#' Marine and Antarctic Studies, of the University of Tasmania for the use
#' of this data.
#'
#' @examples
#' data(blackisland)
#' print(head(blackisland,20))
#' oldpar <- par(no.readonly=TRUE)
#' plot(blackisland$l1,blackisland$dl,type="p",pch=16,
#' xlab="Initial Length mm",ylab="Growth Increment mm",
#' panel.first=grid())
#' abline(h=0)
#' par(oldpar)
NULL
#' @title dataspm A data.frame of fisheries catch and cpue data
#'
#' @description A data.frame containing 31 years of catch, standardized
#' cpue, number of records, and the unstandardized geometric mean
#' cpue for Pink Ling (\emph{Genypterus blacodes}). The fisheries data can
#' be used in the surplus production modelling in Chapter 7. Initial
#' parameter estimates very close to the optimum values could be
#' param <- log(c(r=0.25, K=5500, Binit=3000,sigma=0.2)) for the Schaefer
#' model and log(c(r=0.15, K=6500, Binit=3000, sigma=0.2)) for the Fox model
#'
#' @name dataspm
#'
#' @docType data
#'
#' @format A 31 x 5 data.frame
#' \describe{
#' \item{year}{the year from 1986 t0 2016}
#' \item{catch}{the catch in tonnes to the nearest 100kg}
#' \item{cpue}{the standardized cpue scaled to the mean of the series}
#' \item{records}{the number of records making up the yearly totals}
#' \item{geom}{the naive geometric mean cpue of the raw data as kg/hr, also
#' rescaled to the mean of the series}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Fishery data-set
#' \item Surplus Production Modelling
#' \item Log-Normal likelihoods
#' }
#'
#' @source Haddon, M. and M. Sporcic (2017) Catch rate standardizations for
#' selected SESSF Species (data to 2016) pp 43-406 in Tuck, G.N.
#' (\emph{ed}) \emph{Stock Assessment for the Southern and Eastern scalefish
#' and shark fishery 2016 and 2017.} 837 pp. ISBN 978-1-4863-1012-8 data
#' extracted from Table 7.96 PinkLing4050 page 216.
#'
#' @examples
#' data(dataspm)
#' oldpar <- par(no.readonly=TRUE)
#' plot(dataspm$year,dataspm$geom,type="l",lwd=2,xlab="Year",
#' ylab="CPUE",panel.first=grid())
#' lines(dataspm$year,dataspm$cpue*mean(dataspm$geom),lwd=2,col=2)
#' legend("topright",c("cpue","geom"), col=c(1,2), lwd=3, bty="n",
#' cex=1.2)
#' par(oldpar)
NULL
#' @title LatA Simulated length-at-age for 358 female fish
#'
#' @description A data.frame containing the simulated
#' age for an array of different lengths based upon the properties
#' of an extensive collection of redfish (\emph{Centroberyx affinis})
#' length-at-age data from eastern Australia sampled in the 1990's.
#'
#' @name LatA
#'
#' @docType data
#'
#' @format A data.frame with 358 rows and 2 variables:
#' \describe{
#' \item{age}{simulated ages in years}
#' \item{length}{consequent simulated fork length of the fish, in cms}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Estimating individual growth from length-at-age data
#' \item von Bertalanffy growth curve
#' \item Gompertz growth curve
#' \item Michaelis-Menton curve used as a growth curve
#' }
#'
#' @source The data this simulation is based upon is from length-at-age
#' data for one species collected over many years by the many
#' excellent people running the Integrated Stock Monitoring Program
#' in the Australian South East Fishery over the years of its
#' existence. The simulation is based on a characterization of
#' redfish properties and includes random error in the hypothetical
#' measurements as well as the processes of growth (i.e. both
#' measurement and process error). The other inputs were a selected
#' set of growth parameters and the relative frequency of different
#' ages vs lengths.
#'
#' @examples
#' data(LatA)
#' pars <- c(27.0,0.15,-2.0) # von Bertalanffy
#' bestvB <- nlm(f=ssq,funk=vB,observed=LatA$length,p=pars,
#' ages=LatA$age,typsize=magnitude(pars))
#' outfit(bestvB,backtran=FALSE,title="vB")
NULL
#' @title minnow contains weekly growth data for use with seasonal growth curves
#'
#' @description minnow is a dataset of mean length against time in weeks for
#' minnows (\emph{Phoxinus phoxinus}), derived from Pitcher &
#' Macdonald (1973) for use when fitting growth curves, especially
#' seasonal growth curves. The data exhibit increases and decreases
#' in length as each year progresses because these are mean lengths
#' rather than individual measurements (which would, more typically,
#' be used these days). The data have been read off a graph within
#' the original paper as it is not reported explicitly, and are
#' therefore only approximate, but will do for our purposes (but
#' expect different parameters to those reported in the original
#' paper). This is length at time not age. Though time is being
#' used as a proxy for age there is no age 0.
#'
#' @name minnow
#'
#' @docType data
#'
#' @format A data.frame of mean length-at-time data
#' \describe{
#' \item{week}{the week of sampling minnows for lengths}
#' \item{length}{the estimated mean length in the corresponding week in mm}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item seasonal growth curves
#' \item von Bertalanffy
#' \item Model residuals
#' }
#'
#' @source data measured from Figure 2, page 602 in Pitcher, T.J., and P.D M. MacDonald. (1973)
#' Two models of seasonal growth. \emph{Journal of Applied Ecology} 10:599–606.
#'
#' @examples
#' data(minnow)
#' oldpar <- par(no.readonly=TRUE)
#' plot1(minnow$week,minnow$length,type="p",pch=16,cex=1.2,
#' xlab="Week",ylab="Length mm")
#' par(oldpar)
NULL
#' @title npf fishery catch data from Northern Prawn Fishery 1970-1992
#'
#' @description npf is fishery catch data from Australia's Northern
#' Prawn Fishery from 1970 to 1992 summarized from Robins and
#' Somers, 1994. It contains the catches, in tonnes,
#' of banana prawns (\emph{Penaeus merguiensis} and \emph{P. indicus}),
#' tiger prawns (brown - \emph{P. esculentus}) and (grooved - \emph{P.
#' semisulcatus}), endeavour prawns, (\emph{Metapenaeus endeavouri} and
#' \emph{M. ensis}), king prawns (\emph{P. latisulcatus} and
#' \emph{P. longistylus}), the number of vessels fishing, and
#' the annual effort as boat-days.
#'
#' @name npf
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1970 - 1992.}
#' \item{banana}{banana prawn catches, tonnes.}
#' \item{tiger}{tiger prawn catches, tonnes.}
#' \item{endevaour}{endeavour prawn catches, tonnes.}
#' \item{king}{king prawn catches, tonnes.}
#' \item{boats}{the number of vessels fishing in that year.}
#' \item{boatday}{the total annual effort as boatdays.}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item correlation analysis and regression
#' \item Bootstrap percentile confidence intervals
#' \item Model residuals
#' }
#'
#' @source Robins, C. and I. Somers (1994) Appendix A. Fishery
#' Statistics pp 141 - 164 in Pownall, P.C. (ed.) Australia's
#' Northern Prawn Fishery: The first 25 years. NPF25. Cleveland,
#' Australia. 179p.
#' @examples
#' data(npf)
#' npf
#' oldpar <- par(no.readonly=TRUE)
#' plot1(npf$year,npf$tiger,xlab="Year",ylab="Tonnes",lwd=2)
#' lines(npf$year,npf$endeavour,col=2,lwd=2)
#' legend("topleft",c("Tiger","Endeavour"),col=c(1,2),lwd=3,
#' bty="n",cex=1.5)
#' par(oldpar)
NULL
#' @title pttuna is yellowfin tuna fishery data from Pella-Tomlinson 1969
#'
#' @description pttuna is yellowfin tuna fishery data from Pella-Tomlinson's
#' (1969) classical paper describing their generalized surplus production
#' model. This is the same data as contained in the schaef data-set, except
#' it is extended from 1934 - 1967. Some of the values are slightly
#' different, and their table rounds off the cpue estimates slightly
#' differently but the catch and effort figures are theirs. It contains
#' the year, the catch, the effort, and the cpue (which is just the total
#' catch divided by the total effort, a ratio estimate). Initial parameter
#' estimates close to the optimum values for the Schaefer model could be
#' param <- log(c(r=0.28,K=2100000,Binit=2400000,sigma=0.16)). With this
#' longer time-series the eventual MSY estimate was somewhat larger than
#' when just the schaef data are used.
#'
#' @name pttuna
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1934 - 1967}
#' \item{catch}{the total annual catch, '000s of pounds }
#' \item{effort}{the total effort as standard class 4 baitboat fishing days}
#' \item{cpue}{the catch '000 pounds per standard class 4 day, a ratio cpue}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item surplus production modelling
#' \item classical fisheries data
#' \item Log-Normal likelihoods
#' }
#'
#' @source from Table 6 page 457 in Pella, J.J. and P.K. Tomlinson (1969) A
#' Generalized Stock Production Model. \emph{Bulletin, Inter-American
#' Tropical Tuna Commission} 13(3): 421-458. Obtainable from
#' \emph{https://www.iattc.org/BulletinsENG.htm}
#'
#' @examples
#' data(pttuna)
#' pars <- log(c(r=0.25,K=2.1e06,Binit=2.2e06,sigma=0.2))
#' answer <- fitSPM(pars,pttuna,schaefer=TRUE,maxiter=1000)
#' outfit(answer,title="Pella-Tomlinson Data",digits=4)
NULL
#' @title schaef is yellowfin tuna fishery data from Schaefer 1957
#'
#' @description schaef is yellowfin tuna fishery data from Schaefer
#' (1957) It contains the year, the catch, the effort, and the cpue
#' and was used in one of the first descriptions of a stock
#' assessment that used a surplus production model. The catch-per-
#' unit-effort, cpue, is a ratio cpue of the total catch divided by
#' the total effort as thousands of pounds per day. These days such
#' ratios tend not to be used, with individual records
#' for each day's effort being used instead. Using individual records
#' does not obscure the variation between different
#' vessels, areas, depths, and seasons. Initial parameter estimates
#' close to the optimum values for both the Schaefer model and the
#' Fox model could be
#' param <- log(c(r=0.24,K=2100000,Binit=2200000,sigma=0.2))
#'
#' @name schaef
#'
#' @docType data
#'
#' @format A data.frame of fisheries data
#' \describe{
#' \item{year}{the fishing year from 1934 - 1955}
#' \item{catch}{the total annual catch, '000s of pounds }
#' \item{effort}{the total effort as standard class 4 clipper fishing days}
#' \item{cpue}{the catch '000 pounds per standard class 4 day, a ratio cpue}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item surplus production modelling
#' \item classical fisheries data
#' \item Log-Normal likelihoods
#' }
#'
#' @source from Table 1 page 266 in Schaefer, M.B. (1957) A study of the dynamics of the
#' fishery for yellowfin tuna in the Eastern Tropical Pacific Ocean.
#' Bulletin, Inter-American Tropical Tuna Commission 2: 247-285. Obtainable from
#' \emph{https://www.iattc.org/BulletinsENG.htm}
#'
#' @examples
#' data(schaef)
#' pars <- log(c(r=0.2,K=2.1e06,Binit=2.2e06,sigma=0.2))
#' answer <- fitSPM(pars,schaef,schaefer=TRUE,maxiter=1000)
#' outfit(answer,title="Schaefer, 1957 Data",digits=4)
NULL
#' @title tasab is a matrix of abalone maturity-at-length data
#'
#' @description tasab is a 715 x 4 matrix of maturity-at-length data
#' for blacklip abalone (\emph{Haliotis rubra}) from two sites
#' along the Tasmanian west coast. All data was collected in
#' February 1995, but details, such as site name, accurate
#' location, statistical block, year, month, and other
#' details have been omitted for brevity. See section on maturity
#' within the Static Models chapter for detailed use of this
#' data-set.
#'
#' @name tasab
#'
#' @docType data
#'
#' @format A data.frame of maturity-at-length data
#' \describe{
#' \item{site}{an identifier for the two different sites sampled}
#' \item{sex}{I = immature, M = male, F = female}
#' \item{length}{the shell length in mm}
#' \item{mature}{was the animal mature = 1 or not = 0}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item maturity ogives or logistic curves
#' \item Binomial likelihoods
#' }
#'
#' @source Many thanks to the Institute of Marine and Antarctic Science,
#' which is part of the University of Tasmania, and especially to
#' Dr Craig Mundy, leader of the Abalone Group, for permission to use
#' this data collected in February 1995.
#'
#' @examples
#' data(tasab)
#' head(tasab,20)
#' table(tasab$site,tasab$sex)
NULL
#' @title tigers is tiger prawn recruitment data from Penn & Caputi 1986
#'
#' @description tigers is a dataset of only 14 rows of data with a
#' column of Spawning index and Recruitment index, as a data.frame.
#' The timing of the recruitment index is up to half a year after
#' the spawning index.
#'
#' @name tigers
#'
#' @docType data
#'
#' @format A data.frame of spawning recruitment data
#' \describe{
#' \item{Spawn}{the estimated spawning biomass index in a year (Aug - Oct)}
#' \item{Recruit}{estimated recruitment from the biomass in each year (Mar - May)}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Stock-recruitment curves
#' \item Beverton-Holt and Ricker Models
#' \item Static model fitting
#' }
#'
#' @source Extracted from Table 2, page 496 of Penn, J.W. and N. Caputi
#' (1986) Spawning stock-recruitment relationships and environmental
#' influences on the tiger prawn (\emph{Penaeus esculentus}) fishery
#' in Exmouth Gulf, Western Australia. \emph{Australian Journal of
#' Marine and Freshwater Research} 37: 491-505. Sorted on spawning index.
#'
#' @examples
#' data(tigers)
#' tigers
#' oldpar <- par(no.readonly=TRUE)
#' plot1(tigers$Spawn,tigers$Recruit,type="p",pch=16,cex=1.25)
#' par(oldpar)
NULL
#' @title twoindex has orange roughy catches with hypothetical cpue
#'
#' @description twoindex is a 35 x 4 data.frame of fishery data made
#' up of smoothed real catches but two simulated indices of relative
#' abundance. This data-set is designed to be used to illustrate
#' the implementation of surplus production models when there are
#' more than one time-series of relative abundance indices. This is not
#' currently implemented in the book but is put here for use by readers
#' should they wish to pursue this approach. The
#' indices have been designed to generate a workable answer but also
#' require the use of a penalty on harvest rates to avoid massively
#' inflated harvest rates well above 1. Instead of using simpspm,
#' spm, and negLL1, we need to use simpspmM, spmCE, and negLLM.
#' The cpue series are hypothetical and have been designed to
#' illustrate the use of penalty1 and the use of multiple indices
#' of relative abundance. The real stock assessment uses acoustic
#' survey indices and also uses many years of age composition data
#' inside Stock Synthesis 3, not surprisingly the inclusion of real
#' time-series of indices and of age-composition data leads to very
#' different results.
#'
#' @name twoindex
#'
#' @docType data
#'
#' @format A data.frame of fishery data
#' \describe{
#' \item{year}{the calender year of fishing}
#' \item{catch}{the reported catch in tonnes}
#' \item{cpue1}{the first index of relative abundance}
#' \item{cpue2}{the second index of relative abundance}
#' }
#'
#' @section Subjects:
#' \itemize{
#' \item Surplus production models
#' \item Dynamic model fitting
#' \item -ve log-likelihoods
#' }
#'
#' @source Catches extracted from Table 4, page 11 of Haddon, M. (2017)
#' Orange Roughy East (Hoplostethus atlanticus) stock assessment
#' using data to 2016 Report to November 2017 SE RAG meeting. CSIRO,
#' Oceans and Atmosphere, Australia. 51p. from
#' https://www.afma.gov.au/fisheries-management/species/orange-roughy
#' Catch data extended to 2019 using AFMA's catchwatch system.
#'
#' @examples
#' data(twoindex)
#' fish <- as.matrix(twoindex)
#' pars <- log(c(0.04,155000,0.4,0.3))
#' bestSP <- nlm(f=negLLM,p=pars,funk=simpspmM,indat=fish,
#' schaefer=TRUE,logobs=log(fish[,c("cpue1","cpue2")]),
#' steptol=1e-06,harvpen=TRUE)
#' namepar <- c("r", "K", "Binit","sigma")
#' outfit(bestSP,parnames=namepar) # best fitting estimates
#' # if 'Either ~local min or steptol too small try 'steptol=1e-05'
#' # plotprep(width=7,height=5,newdev=FALSE) # for external plot
#' answer <- plotspmmod(bestSP$estimate,indat=fish,
#' plotprod=TRUE,maxy=3.4)
NULL
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