#-------------------------------------------------------------------------------
# FLBDsim class.
# Created: Sonia SYYYnchez - 16/08/2010 12:37:18
# Changed: 26/10/2010 08:40:46 (dorleta garcia)
# Changed: 30/09/2016 09:30:01 (Agurtzane Urtizberea)
#-------------------------------------------------------------------------------
# An object to simulate biomass (when using production models).
validFLBDsim <- function(object){
## All FLQuant objects must have same dimensions, including 'iter'.
Dim <- dim(object@biomass)
s. <-list("biomass","catch","uncertainty")
for (i. in s.) {
t. <- slot(object,i.)
if (is.FLQuant(t.) & !all(dim(t.) == Dim))
stop(paste("FLQuant dimensions wrong for ", i.))
}
## FLQuant in covar all the same dimensions.
for(i in names(object@covar)){
if (!all(dim(object@covar[[i]]) == Dim))
stop(paste("FLQuant dimensions in 'covar' list wrong for ", i.))
}
# Params.
if(!(all(dim(object@params)[-1] == Dim[c(2,4,6)])))
stop(cat("Wrong dimension in 'params', it should be equalt to : npar", Dim[c(2,4,6)], '\n'))
# model
if(length(object@model) != 1)
stop("Wrong length in 'model', it must be equal 1")
if(length(grep('~', object@model)) == 0 & class(eval(call(object@model))[[2]]) != 'formula')
stop("Specified 'model' is not defined in 'FLCore' or the specified formula is not correct")
# check that the number of params and the names are consistent with the selected model
# npar <- all.vars(get(object@model, pos = 1)()[[2]])
# npar <- npar[!(npar %in% c('biomass','bprod'))]
# if ( dim(object@params)[1] != length(npar) )
# stop(cat("For default model 'PellaTom', the number of parameters should be 3 (", npar,")", '\n'))
# for (p in npar) if ( !(p %in% dimnames(object@params)$params))
# stop(cat("Parameters should be:", npar, '\n'))
# Everything is fine
return(TRUE)
}
#' @name FLBDsim
#' @rdname FLBDsim
#' @aliases FLBDsim FLBDsim-class
#'
#' @title FLBDsim class and methods
#'
#' @description A class to simulate the growth of populations aggregated in biomass.
#'
#' @param ... Empty or FLQuants for 'biomass', 'catch' and 'uncertainty' slots and optionally the values for the rest of the slots.
#'
#' @details The FLBDsim has the following slots:
#'
#' @slot name The name of the stock.
#' @slot desc A description of the object.
#' @slot range The range of the object.
#' @slot biomass An FLQuant to store the biomass of the stock.
#' @slot gB An FLQuant to store the surplus production of the stock.
# @sdb sdb standard deviation of the process error in a continuos prodyction model (SPiCT). It adds the term -0.5*sdb*Delta_t to the stock dynamics equation.
#' @slot catch An FLQuant to store the catch of the stock.
#' @slot uncertainty An FLQuant to store the uncertainty that is multiplied to the biomass in every step of the simulation.
#' @slot covar An FLQuants to store the covariates that are part of the growth model.
#' @slot model A character with the name of the model to simulate the recruitment process.
#' @slot params An array with dimension [numb.params, numb.year, numd.season, numb.iteration] with year and season and iteration dependent parameters of the growth model.
#' @slot alpha An array with dimension [numb.year, numd.season, numb.iteration] with year, season and iteration dependent value bigger than one which indicates, in percentage, how big can be the biomass in comparison with the carrying capacity.
#' @slot name The name of the object.
#' @slot desc Character with the description of the object.
#' @slot range A numeric vector with the range of the object as in other FLR objects.
#'
#' @return An object of class FLBDsim.
setClass("FLBDsim",
representation(
"FLComp",
biomass = "FLQuant", # [1,ny,1,ns,1,it]
gB = "FLQuant", # [1,ny,1,ns,1,it]
catch = "FLQuant", # [1,ny,1,ns,1,it]
covar = "FLQuants", # [1,ny,1,ns,1,it]
# sdb = "FLQuant", # [1,ny,1,ns,1,it]
uncertainty = "FLQuant", # [1,ny,1,ns,1,it]
model = "character", # [it] - different model by iteration.
params = "array", # array[param, year, season, iteration] # year in order to model regime shifts.
alpha = "array" # array[year, season, iteration] [1]
),
prototype=prototype(
name =character(0),
desc =character(0),
range =unlist(list(min=NA, max=NA, plusgroup=NA, minyear=1, maxyear=1)),
biomass = FLQuant(), # [1,ny,1,ns,1,it]
gB = FLQuant(),
catch = FLQuant(), # [1,ny,1,ns,1,it]
covar = FLQuants(), # [1,ny,1,ns,1,it]
# sdb = FLQuant(), # [1,ny,1,ns,1,it]
uncertainty = FLQuant(), # [1,ny,1,ns,1,it]
model = as.character(NA), # [it] - different model by iteration.
params = array(), # array[param, year, season, iteration] # year in order to model regime shifts.
alpha = array(), # [1]
validity=validFLBDsim
))
setValidity("FLBDsim", validFLBDsim)
remove(validFLBDsim) # We do not need this function any more
#invisible(createFLAccesors("FLBDSim", exclude=c('name', 'desc', 'range'))) # }}}
FLBDsim <- function(...){
a <- new('FLBDsim')
x <- list(...)
attach(x, pos = 2, warn.conflicts = FALSE)
slots <- names(x)
if(all(slots %in% c('name', 'desc', 'range'))){ # => NO DIMENSIONS
for(sl in slots){
slot(a, sl) <- x[sl][[1]]
}
return(a)
}
# ELSE, CORRECT DIMENSIONS ARE NEEDED IN THE DIMENSIONAL SLOTS!!!
ny <- ns <- ni <- 1
nmy <- nmi <- 1
nms <- 'all'
nmparams <- 'a'
quants <- c('biomass', 'catch', 'uncertainty') #, 'sdb')
if(any(slots %in% quants)){
Dim <- dim(get(slots[which(slots %in% quants)[1]], pos = 2))
Dimnms <- dimnames(get(slots[which(slots %in%quants)[1]], pos = 2))
ny <- Dim[2]; nmy <- Dimnms[[2]]
ns <- Dim[4]; nms <- Dimnms[[4]]
ni <- Dim[6]; nmi <- Dimnms[[6]]
}
else{ # slots in "covar" "model" "params" "name" "desc" "range"
if(any(slots == 'covar')){
Dim <- dim(x[['covar']][[1]])
Dimnms <- dimnames(x[['covar']][[1]])
ny <- Dim[2]; nmy <- Dimnms[[2]]
ns <- Dim[4]; nms <- Dimnms[[4]]
ni <- Dim[6]; nmi <- Dimnms[[6]]
}
else{
if(any(slots == 'params')){
Dim <- dim(x[['params']])
Dimnms <- dimnames(x[['params']])
ny <- Dim[2]; nmy <- Dimnms[[2]]
ns <- Dim[3]; nms <- Dimnms[[3]]
ni <- Dim[4]; nmi <- Dimnms[[4]]
}
else{ # => model
Dim <- length(x[['model']])
ni <- Dim ; nmi <- 1:Dim
}
}
}
if('model' %in% slots){
nmparams <- all.vars(get(x[['model']])()[[2]])
nmparams <- nmparams[!(nmparams %in% c('biomass','bprod'))]
}
detach()
for(sl in slots){ # fill the slots that are in the call.
slot(a,sl) <- x[sl][[1]]
}
# dimensional slots that are not in the call.
# if covar is not in the call => covar :: EMPTY.
noin <- slotNames(a)[-which((slotNames(a) %in% slots))]
noin <- noin[! noin %in% c('name', 'desc', 'range', 'covar')]
for(sl in noin){
if(sl %in% quants){
y <- ifelse(sl == 'uncertainty', 1, ifelse(sl == 'sdb', 0, NA))
xx <- ifelse(sl == 'biomass', 0, 'all')
slot(a,sl) <- FLQuant(y,dim = c(1,ny,1,ns,1,ni), dimnames = list(age = 'all', year = nmy, season = nms, iter = nmi))
}
else
if(sl == 'model') slot(a,sl) <- 'PellaTom'
else
if(sl == 'params') {
nmparams <- all.vars(get('PellaTom')()[[2]])
nmparams <- nmparams[!(nmparams %in% c('biomass','bprod'))]
slot(a, sl) <- array(dim = c(length(nmparams),ny,ns,ni), dimnames = list(params = nmparams, year = nmy, season = nms, iter = nmi))
}
}
a@alpha <- array((a@params['p',,,]/a@params['r',,,]+1)^(1/a@params['p',,,]),
dim = c(ny,ns,ni))
validObject(a)
return(a)
}
# BDsim {{{
# It fills in the biomass slot for only 1 year and 1 season.
# Otherwise we would have ny*ns combinations and for simulation we simulate one by one!
# iter >= 1.
BDsim <- function(object, year = 1, season = 1, iter = 'all') # year and season either numeric (position) or character (name)
{
Dim <- dim(object@biomass)
dimnms <- dimnames(object@biomass)
# If year/season/iter numerics => indicate position
# else names => get positions.
if(length(year) > 1 | length(season) > 1)
stop('Only one year and season is allowed' )
# 'year' dimension.
yr <- year
if(is.character(year)) yr <- which(dimnms[[2]] %in% year)
if(length(yr) == 0) stop('The year is outside object time range')
# 'season' dimension.
ss <- season
if(is.character(season)) ss <- which(dimnms[[4]] %in% season)
if(length(ss) == 0) stop('The season is outside object season range')
# 'iter' dimension.
if(iter == 'all') it <- 1:Dim[6]
else if(is.character(iter)) it <- which(dimnms[[6]] %in% iter)
if(length(it) == 0 | length(it) < length(iter)) stop('Some of all iterations are outside object iteration range')
# Previous season/year.
if(ss == 1){
yr0 <- yr-1
ss0 <- Dim[4]
}
else{
yr0 <- yr
ss0 <- ss -1
}
# model call
if(length(grep('~', object@model)) == 0)
model <- eval(call(object@model))[[2]]
else # character but 'formula'
model <- formula(object@model)
model <- as.list(model)[[3]]
# Extract biomass # numeric[1 OR it]
datam <- list(biomass = c(object@biomass[,yr0,,ss0,]))
# Extract catches # numeric[1 OR it]
# datam[['catch']] <- c(object@catch[,yr,,ss,])
# Extract covars.
for(i in names(object@covar))
datam[[i]] <- c(object@covar[[i]][,yr0,, ss0,])
# Extract params
for(i in dimnames(object@params)[[1]])
datam[[i]] <- c(object@params[i,yr0,ss0,])
# res <- numeric(Dim[6])
# for(i in 1:Dim[6])
res <- eval(model, datam) # growth
newB <- object@biomass[,yr0,,ss0,] - object@catch[,yr0,,ss0,] + res*object@uncertainty[,yr0,,ss0,] #- 0.5*object@sdb[,yr0,,ss0,]^2*object@biomass[,yr0,,ss0,]
if(object@model=="PellaTom"){
newB <- ifelse((object@biomass[,yr0,,ss0,]+ res*object@uncertainty[,yr0,,ss0,])>
(object@alpha[yr0,ss0,]*object@params["K",yr0,ss0,]),
(object@alpha[yr0,ss0,]*object@params["K",yr0,ss0,]) - object@catch[,yr0,,ss0,], # - 0.5*object@sdb[,yr0,,ss0,]^2*object@biomass[,yr0,,ss0,],
newB)
}
object@gB[,yr0,,ss0,] <- newB + object@catch[,yr0,,ss0,] - object@biomass[,yr0,,ss0,] # + 0.5*object@sdb[,yr0,,ss0,]^2*object@biomass[,yr0,,ss0,]
object@biomass[,yr,,ss,] <- newB
# 2017/03/16 update gB in year 'yr'
# Extract biomass # numeric[1 OR it]
datam <- list(biomass = c(object@biomass[,yr,,ss,]))
# Extract catches # numeric[1 OR it]
# datam[['catch']] <- c(object@catch[,yr,,ss,])
# Extract covars.
for(i in names(object@covar))
datam[[i]] <- c(object@covar[[i]][,yr,, ss,])
# Extract params
for(i in dimnames(object@params)[[1]])
datam[[i]] <- c(object@params[i,yr,ss,])
res <- eval(model, datam)
object@gB[,yr,,ss,] <- res*object@uncertainty[,yr,,ss,]
return(object)
} # }}}
#
# # FLBDsim
# setGeneric('FLBDsim', function(x,...) standardGeneric('FLBDsim'))
#
# ## FLBDsim() {{{
# # FLBDsim(FLCatch)
# setMethod('FLBDsim', signature(x='FLQuant'),
# function(x, ...){
# FLBDsim(biomass=x, catch = x, uncertainty = x, ...)
#
# }
# )
#
# ## FLBDsim() {{{
# # FLBDsim(FLCatch)
# setMethod('FLBDsim', signature(x='character'),
# function(x, ...){
# FLBDsim(name=x, ...)
#
# }
# )
#
#
# setGeneric('FLBDsim', function(biomass, catch, uncertainty, name,...) standardGeneric('FLBDsim'))
#
# ## FLBDsim() {{{
# # FLBDsim(FLCatch)
# setMethod('FLBDsim', signature(name='character', biomass = 'FLQuant', uncertainty = 'FLQuant', catch = 'FLQuant'),
# function(biomass, catch, uncertainty, name, ...){
# FLBDsim(name=name, biomass = biomass, catch = catch, uncertainty = uncertainty,...)
#
# }
# )
## Pella-Tomlinson surplus production model
# (from: Jennings et al., 2001. Marine Fisheries Ecology. Blackwell Science, Oxford, UK.)
PellaTom <- function(){
## log likelihood, assuming normal log.
logl <- function( biomass, bprod, r, K, p)
loglAR1( log(bprod), log(biomass * (r/p) * (1 - (biomass/K)^p)))
## model to be fitted
model <- bprod ~ biomass * (r/p) * (1 - (biomass/K)^p)
# Output
return( list(logl=logl, model=model) )
}
## iter {{{
setMethod("iter", signature(obj="FLBDsim"),
function(obj, iter)
{
# FLQuant slots
names <- names(getSlots(class(obj))[getSlots(class(obj))=="FLQuant"])
for(s in names)
{
if(dims(slot(obj, s))$iter == 1)
slot(obj, s) <- iter(slot(obj, s), 1)
else
slot(obj, s) <- iter(slot(obj, s), iter)
}
# covar
if(length(obj@covar) > 0) slot(obj, 'covar') <- iter(slot(obj, 'covar'), iter)
#params
slot(obj, 'params') <- slot(obj, 'params')[,,,iter,drop=F]
dimnames(slot(obj, 'params'))[[4]] <- 1:length(iter)
#alpha
slot(obj, 'alpha') <- slot(obj, 'alpha')[,,iter,drop=F]
dimnames(slot(obj, 'alpha'))[[3]] <- 1:length(iter)
return(obj)
}
) # }}}
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