R/create_lh_list.R
In merrillrudd/LIME: Length-based Integrated Mixed Effects (LIME) assessment method
Defines functions
create_lh_list
Documented in
create_lh_list
#' Create new life history list
#'
#' \code{create_lh_list} Creates list of life history information
#'
#' @author M.B. Rudd
#' @param vbk von Bertalanffy k Brody growth coefficient
#' @param linf von Bertalanffy Linf asymptotic length
#' @param lwa length-weight scaling parameter
#' @param lwb length-weight allometric parameter
#' @param M value for natural mortality if there has been a study (default = NULL, calculated internally from vbk)
#' @param AgeMax option to specify maximum age; default=NULL will calculate as the age at which 1 percent of individuals are left in the unfished condition
#' @param S50 starting value for age or length at 50 percent selectivity (will be estimated in LIME method) -- can be vector for multiple fleets
#' @param M50 age or length at 50 percent maturity
#' @param S95 default=NULL for one-parameter logistic model; starting value for age or length at 95 percent selectivity -- can be vector for multiple fleets
#' @param M95 default=NULL for one-parameter logistic model; age or length at 50 percent maturity
#' @param Sslope default=NULL, option to specify slope of logistic curve for length-at-selectivity -- can be vector for multiple fleets
#' @param Mslope default=NULL option to specify slope of logistic curve for length-at-maturity
#' @param selex_input specify whether argument S50 is an age or a length (default length)
#' @param maturity_input specify whether argument M50 is an age or a length (default length)
#' @param selex_type default="logistic" for 1-parameter logistic selex, alternate="dome" for dome-shaped selectivity and must specify dome-params LV and RV. -- can be vector for multiple fleets
#' @param dome_sd standard deviation of normal distribution to the right side of the fully selected age/length -- can be vector for multiple fleets
#' @param binwidth width of length bins (default = 1)
#' @param t0 theoretical age at length=0 (default = -0.01); avoid fixing to zero due to some issues with the first age/length bin
#' @param R0 equilibrium recruitment (default = 1); when no information on scale is available, will estimate relative deviations around equilibrium 1
#' @param h steepness parameter (default = 1)
#' @param CVlen CV of the growth curve (default = 0.1)
#' @param SigmaC standard deviation - observation error of catch data (default = 0.2)
#' @param SigmaI standard deviation - observation error of index data (default = 0.2)
#' @param SigmaR standard deviation - process error for recruitment time series (default = 0.6 -- starting value, will be estimated)
#' @param SigmaF standard deviation - process error for fishing mortality time series (default = 0.3) -- can be vector for multiple fleets
#' @param qcoef starting value for catchability coefficient (when index data is available, default = 1e-5) -- can be vector for multiple fleets
#' @param Fequil equilibrium fishing mortality rate (used for simulation; default=0.2) -- can be vector for multiple fleets
#' @param Frate parameter used to simulate fishing moratality time series (default=NULL) -- can be vector for multiple fleets
#' @param start_ages age to start (either 0 or 1; default = 0)
#' @param rho first-order autocorrelation in recruitment residuals parameter, default=0 (recruitment not autocorrelated)
#' @param theta dirichlet-multinomial parameter related to effective sample size. default to 10, will not be used if length frequency distribution LFdist is set to multinomial (0). Only used if distribution is dirichlet-multinomial (LFdist=1)
#' @param nseasons specify number of sub-time periods per year; default=1 (instantaneous sampling)
#' @param nfleets specify number of fleets - fleet-specific parameters can be length nfleets, or shared by specifying only one number
#' @importFrom stats pnorm
#'
#'
#' @return List, a tagged list of life history traits
#' @export
create_lh_list <-
function(vbk,
linf,
lwa,
lwb,
S50,
M50,
S95=NULL,
M95=NULL,
Sslope=NULL,
Mslope=NULL,
selex_input="length",
maturity_input="length",
selex_type="logistic",
dome_sd=NULL,
binwidth=1,
t0=-0.01,
CVlen=0.1,
SigmaC=0.001,
SigmaI=0.001,
SigmaR=0.737,
SigmaF=0.2,
R0=1,
h=1,
qcoef=1e-5,
M=NULL,
AgeMax=NULL,
Fequil=0.5,
Frate=0.2,
start_ages=0,
rho=0,
theta=1,
nseasons=1,
nfleets=1){
## mortality
if(is.null(M)) M <- 1.5*vbk ## based on vbk if not specified
if(is.null(AgeMax)) AgeMax <- ceiling(-log(0.01)/M)
ages <- seq(start_ages, to=(AgeMax+1 - (1/nseasons)), by=(1/nseasons))
## monthly mortality
M <- M/nseasons
## length bins
mids <- seq((binwidth/2), linf*1.3, by=binwidth)
highs <- mids + (binwidth/2)
lows <- mids - (binwidth)/2
## growth at age
L_a <- linf*(1-exp(-vbk*(ages - t0)))
W_a <- lwa*L_a^lwb
W_l <- lwa*mids^lwb
##probability of being in a length bin given age
lbprobs <- function(mnl,sdl) return(pnorm(highs,mnl,sdl)-pnorm(lows,mnl,sdl))
vlprobs <- Vectorize(lbprobs,vectorize.args=c("mnl","sdl"))
plba_a <- t(vlprobs(L_a,L_a*CVlen))
plba_a <- plba_a/rowSums(plba_a)
## maturity and selectivity
if(is.null(M95)) mat_param <- 1
if(is.null(M95)==FALSE) mat_param <- 2
if(is.null(Mslope)==FALSE) mat_param <- 3
sel_param <- rep(1, nfleets)
if(is.null(S95)==FALSE){
sel_param[which(is.na(S95))] <- 1
sel_param[which(is.na(S95)==FALSE)] <- 2
}
if(is.null(Sslope)==FALSE){
sel_param[which(is.na(Sslope)==FALSE)] <- 3
}
if(selex_input=="length"){
SL50 <- S50
S50 <- ceiling(t0-log(1-(SL50/linf))/vbk)
if(is.null(S95)==FALSE){
SL95 <- S95
S95 <- sapply(1:length(SL95), function(x) ceiling(t0-log(1-(SL95[x]/linf))/vbk))
}
}
if(selex_input=="age"){
SL50 <- ceiling(linf*(1-exp(-vbk*(S50-t0))))
if(is.null(S95)==FALSE) SL95 <- sapply(1:length(S95), function(x) ceiling(linf*(1-exp(-vbk*(S95[x]-t0)))))
}
if(maturity_input=="length"){
ML50 <- M50
M50 <- ceiling(t0-log(1-(ML50/linf))/vbk)
if(is.null(M95)==FALSE){
ML95 <- M95
M95 <- ceiling(t0-log(1-(ML95/linf))/vbk)
}
}
if(maturity_input=="age"){
ML50 <- ceiling(linf*(1-exp(-vbk*(M50-t0))))
if(is.null(M95)==FALSE) ML95 <- ceiling(linf*(1-exp(-vbk*(M95-t0))))
}
## maturity
if(mat_param==1){
## specified length bins
Mat_l <- (1 / (1 + exp(ML50 - mids)))
## 1 cm length bins default
# Mat_l2 <- 1 / (1 + exp(ML50 - mids2))
}
if(mat_param==2){
Mat_l <- (1 /(1 + exp(-log(19)*(mids-ML50)/(ML95-ML50)))) # Maturity at length
# Mat_l2 <- 1 /(1 + exp(-log(19)*(mids2-ML50)/(ML95-ML50)))
}
if(mat_param==3){
Mat_l <- (1 /(1 + exp(-((mids-ML50)/Mslope))))
}
if(maturity_input=="length"){
Mat_a <- apply(t(plba_a)*Mat_l, 2, sum)
}
if(maturity_input=="age"){
Mat_a <- rep(NA, length(ages))
if(mat_param==1){
Mat_a <- 1/(1+exp(M50 - ages))
}
if(mat_param==2){
Mat_a <- 1/(1+exp(-log(19)*(ages-M50)/(M95-M50)))
}
}
if(is.null(M95)){
id_L95 <- which(round(Mat_a, 2) %in% seq(from=0.92,to=1.00,by=0.01))[1]
ML95 <- L_a[id_L95]
M95 <- ceiling(t0-log(1-(ML95/linf))/vbk)
}
S_fl <- matrix(NA, nrow=nfleets, ncol=length(mids))
S_fa <- matrix(NA, nrow=nfleets, ncol=length(ages))
## selectivity-at-length
if(any(sel_param==1)){
index <- which(sel_param==1)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 / (1 + exp(SL50[index[i]] - mids)))
}
}
if(any(sel_param==2)){
index <- which(sel_param==2)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 /(1 + exp(-log(19)*(mids-SL50[index[i]])/(SL95[index[i]]-SL50[index[i]]))))
}
}
if(any(sel_param==3)){
index <- which(sel_param==3)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 /(1 + exp(-((mids-SL50[index[i]])/Sslope[index[i]]))))
}
}
if(start_ages==0){
S_fl[,1] <- 1e-5
}
if(any(selex_type=="dome")){
index <- which(selex_type=="dome")
for(i in 1:length(index)){
Sfull <- which(round(S_fl[index[i],],2)==1.00)[1]
if(is.na(Sfull)) Sfull <- which(round(S_fl[index[i],],1)==1.00)[1]
find_dome <- (Sfull+1):length(S_fl[index[i],])
S_fl[index[i],find_dome] <- exp((-(find_dome-Sfull)^2)/(2*dome_sd[index[i]]^2))
}
}
S_fa <- t(sapply(1:nfleets, function(x){
colSums(t(plba_a)*S_fl[x,])
}))
if(start_ages==0){
S_fa[,1] <- 1e-5
}
if(any(selex_type=="dome")==FALSE) Sfull <- NULL
S_fl_out <- data.frame("Variable"="Selectivity", "By"="Length", "X"=c(sapply(1:ncol(S_fl), function(x) rep(mids[x], nfleets))), "Value"=c(S_fl), "Fleet"=rep(1:nfleets, ncol(S_fl)))
W_l_out <- data.frame("Variable"="Weight", "By"="Length", "X"=mids, "Value"=W_l, "Fleet"=0)
Mat_l_out <- data.frame("Variable"="Maturity", "By"="Length", "X"=mids, "Value"=Mat_l, "Fleet"=0)
S_fa_out <- data.frame("Variable"="Selectivity", "By"="Age", "X"=c(sapply(1:ncol(S_fa), function(x) rep(ages[x], nfleets))), "Value"=c(S_fa), "Fleet"=rep(1:nfleets, ncol(S_fa)))
L_a_out <- data.frame("Variable"="Length", "By"="Age", "X"=ages, "Value"=L_a, "Fleet"=0)
W_a_out <- data.frame("Variable"="Weight", "By"="Age", "X"=ages, "Value"=W_a, "Fleet"=0)
Mat_a_out <- data.frame("Variable"="Maturity", "By"="Age", "X"=ages, "Value"=Mat_a, "Fleet"=0)
df <- rbind(S_fl_out, W_l_out, Mat_l_out, S_fa_out, L_a_out, W_a_out, Mat_a_out)
df$Fleet <- as.factor(df$Fleet)
## output list
Outs <- NULL
Outs$df <- df
Outs$vbk <- vbk
Outs$linf <- linf
Outs$t0 <- t0
Outs$binwidth <- binwidth
Outs$CVlen <- CVlen
Outs$SigmaC <- SigmaC
Outs$SigmaI <- SigmaI
Outs$SigmaR <- SigmaR
Outs$SigmaF <- SigmaF
Outs$R0 <- R0
Outs$lwa <- lwa
Outs$lwb <- lwb
Outs$S50 <- S50
Outs$S95 <- S95
Outs$SL50 <- SL50
Outs$SL95 <- SL95
Outs$Sfull <- Sfull
Outs$dome_sd <- dome_sd
Outs$selex_type <- selex_type
Outs$selex_input <- selex_input
Outs$plba <- plba_a
Outs$maturity_input <- maturity_input
Outs$h <- h
Outs$qcoef <- qcoef
Outs$M <- M
Outs$AgeMax <- AgeMax
Outs$ages <- ages
Outs$mids <- mids
Outs$highs <- highs
Outs$lows <- lows
Outs$S_fa <- S_fa
Outs$S_fl <- S_fl
Outs$L_a <- L_a
Outs$W_a <- W_a
Outs$W_l <- W_l
Outs$M50 <- M50
Outs$M95 <- M95
Outs$ML50 <- ML50
Outs$ML95 <- ML95
Outs$Mat_a <- Mat_a
Outs$Mat_l <- Mat_l
Outs$Fequil <- Fequil/nseasons
Outs$Frate <- Frate
Outs$rho <- rho
Outs$theta <- theta
Outs$nseasons <- nseasons
Outs$nfleets <- nfleets
return(Outs)
}
merrillrudd/LIME documentation built on June 20, 2020, 2:58 p.m.
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Defines functions create_lh_list
Documented in create_lh_list
#' Create new life history list
#'
#' \code{create_lh_list} Creates list of life history information
#'
#' @author M.B. Rudd
#' @param vbk von Bertalanffy k Brody growth coefficient
#' @param linf von Bertalanffy Linf asymptotic length
#' @param lwa length-weight scaling parameter
#' @param lwb length-weight allometric parameter
#' @param M value for natural mortality if there has been a study (default = NULL, calculated internally from vbk)
#' @param AgeMax option to specify maximum age; default=NULL will calculate as the age at which 1 percent of individuals are left in the unfished condition
#' @param S50 starting value for age or length at 50 percent selectivity (will be estimated in LIME method) -- can be vector for multiple fleets
#' @param M50 age or length at 50 percent maturity
#' @param S95 default=NULL for one-parameter logistic model; starting value for age or length at 95 percent selectivity -- can be vector for multiple fleets
#' @param M95 default=NULL for one-parameter logistic model; age or length at 50 percent maturity
#' @param Sslope default=NULL, option to specify slope of logistic curve for length-at-selectivity -- can be vector for multiple fleets
#' @param Mslope default=NULL option to specify slope of logistic curve for length-at-maturity
#' @param selex_input specify whether argument S50 is an age or a length (default length)
#' @param maturity_input specify whether argument M50 is an age or a length (default length)
#' @param selex_type default="logistic" for 1-parameter logistic selex, alternate="dome" for dome-shaped selectivity and must specify dome-params LV and RV. -- can be vector for multiple fleets
#' @param dome_sd standard deviation of normal distribution to the right side of the fully selected age/length -- can be vector for multiple fleets
#' @param binwidth width of length bins (default = 1)
#' @param t0 theoretical age at length=0 (default = -0.01); avoid fixing to zero due to some issues with the first age/length bin
#' @param R0 equilibrium recruitment (default = 1); when no information on scale is available, will estimate relative deviations around equilibrium 1
#' @param h steepness parameter (default = 1)
#' @param CVlen CV of the growth curve (default = 0.1)
#' @param SigmaC standard deviation - observation error of catch data (default = 0.2)
#' @param SigmaI standard deviation - observation error of index data (default = 0.2)
#' @param SigmaR standard deviation - process error for recruitment time series (default = 0.6 -- starting value, will be estimated)
#' @param SigmaF standard deviation - process error for fishing mortality time series (default = 0.3) -- can be vector for multiple fleets
#' @param qcoef starting value for catchability coefficient (when index data is available, default = 1e-5) -- can be vector for multiple fleets
#' @param Fequil equilibrium fishing mortality rate (used for simulation; default=0.2) -- can be vector for multiple fleets
#' @param Frate parameter used to simulate fishing moratality time series (default=NULL) -- can be vector for multiple fleets
#' @param start_ages age to start (either 0 or 1; default = 0)
#' @param rho first-order autocorrelation in recruitment residuals parameter, default=0 (recruitment not autocorrelated)
#' @param theta dirichlet-multinomial parameter related to effective sample size. default to 10, will not be used if length frequency distribution LFdist is set to multinomial (0). Only used if distribution is dirichlet-multinomial (LFdist=1)
#' @param nseasons specify number of sub-time periods per year; default=1 (instantaneous sampling)
#' @param nfleets specify number of fleets - fleet-specific parameters can be length nfleets, or shared by specifying only one number
#' @importFrom stats pnorm
#'
#'
#' @return List, a tagged list of life history traits
#' @export
create_lh_list <-
function(vbk,
linf,
lwa,
lwb,
S50,
M50,
S95=NULL,
M95=NULL,
Sslope=NULL,
Mslope=NULL,
selex_input="length",
maturity_input="length",
selex_type="logistic",
dome_sd=NULL,
binwidth=1,
t0=-0.01,
CVlen=0.1,
SigmaC=0.001,
SigmaI=0.001,
SigmaR=0.737,
SigmaF=0.2,
R0=1,
h=1,
qcoef=1e-5,
M=NULL,
AgeMax=NULL,
Fequil=0.5,
Frate=0.2,
start_ages=0,
rho=0,
theta=1,
nseasons=1,
nfleets=1){
## mortality
if(is.null(M)) M <- 1.5*vbk ## based on vbk if not specified
if(is.null(AgeMax)) AgeMax <- ceiling(-log(0.01)/M)
ages <- seq(start_ages, to=(AgeMax+1 - (1/nseasons)), by=(1/nseasons))
## monthly mortality
M <- M/nseasons
## length bins
mids <- seq((binwidth/2), linf*1.3, by=binwidth)
highs <- mids + (binwidth/2)
lows <- mids - (binwidth)/2
## growth at age
L_a <- linf*(1-exp(-vbk*(ages - t0)))
W_a <- lwa*L_a^lwb
W_l <- lwa*mids^lwb
##probability of being in a length bin given age
lbprobs <- function(mnl,sdl) return(pnorm(highs,mnl,sdl)-pnorm(lows,mnl,sdl))
vlprobs <- Vectorize(lbprobs,vectorize.args=c("mnl","sdl"))
plba_a <- t(vlprobs(L_a,L_a*CVlen))
plba_a <- plba_a/rowSums(plba_a)
## maturity and selectivity
if(is.null(M95)) mat_param <- 1
if(is.null(M95)==FALSE) mat_param <- 2
if(is.null(Mslope)==FALSE) mat_param <- 3
sel_param <- rep(1, nfleets)
if(is.null(S95)==FALSE){
sel_param[which(is.na(S95))] <- 1
sel_param[which(is.na(S95)==FALSE)] <- 2
}
if(is.null(Sslope)==FALSE){
sel_param[which(is.na(Sslope)==FALSE)] <- 3
}
if(selex_input=="length"){
SL50 <- S50
S50 <- ceiling(t0-log(1-(SL50/linf))/vbk)
if(is.null(S95)==FALSE){
SL95 <- S95
S95 <- sapply(1:length(SL95), function(x) ceiling(t0-log(1-(SL95[x]/linf))/vbk))
}
}
if(selex_input=="age"){
SL50 <- ceiling(linf*(1-exp(-vbk*(S50-t0))))
if(is.null(S95)==FALSE) SL95 <- sapply(1:length(S95), function(x) ceiling(linf*(1-exp(-vbk*(S95[x]-t0)))))
}
if(maturity_input=="length"){
ML50 <- M50
M50 <- ceiling(t0-log(1-(ML50/linf))/vbk)
if(is.null(M95)==FALSE){
ML95 <- M95
M95 <- ceiling(t0-log(1-(ML95/linf))/vbk)
}
}
if(maturity_input=="age"){
ML50 <- ceiling(linf*(1-exp(-vbk*(M50-t0))))
if(is.null(M95)==FALSE) ML95 <- ceiling(linf*(1-exp(-vbk*(M95-t0))))
}
## maturity
if(mat_param==1){
## specified length bins
Mat_l <- (1 / (1 + exp(ML50 - mids)))
## 1 cm length bins default
# Mat_l2 <- 1 / (1 + exp(ML50 - mids2))
}
if(mat_param==2){
Mat_l <- (1 /(1 + exp(-log(19)*(mids-ML50)/(ML95-ML50)))) # Maturity at length
# Mat_l2 <- 1 /(1 + exp(-log(19)*(mids2-ML50)/(ML95-ML50)))
}
if(mat_param==3){
Mat_l <- (1 /(1 + exp(-((mids-ML50)/Mslope))))
}
if(maturity_input=="length"){
Mat_a <- apply(t(plba_a)*Mat_l, 2, sum)
}
if(maturity_input=="age"){
Mat_a <- rep(NA, length(ages))
if(mat_param==1){
Mat_a <- 1/(1+exp(M50 - ages))
}
if(mat_param==2){
Mat_a <- 1/(1+exp(-log(19)*(ages-M50)/(M95-M50)))
}
}
if(is.null(M95)){
id_L95 <- which(round(Mat_a, 2) %in% seq(from=0.92,to=1.00,by=0.01))[1]
ML95 <- L_a[id_L95]
M95 <- ceiling(t0-log(1-(ML95/linf))/vbk)
}
S_fl <- matrix(NA, nrow=nfleets, ncol=length(mids))
S_fa <- matrix(NA, nrow=nfleets, ncol=length(ages))
## selectivity-at-length
if(any(sel_param==1)){
index <- which(sel_param==1)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 / (1 + exp(SL50[index[i]] - mids)))
}
}
if(any(sel_param==2)){
index <- which(sel_param==2)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 /(1 + exp(-log(19)*(mids-SL50[index[i]])/(SL95[index[i]]-SL50[index[i]]))))
}
}
if(any(sel_param==3)){
index <- which(sel_param==3)
for(i in 1:length(index)){
S_fl[index[i],] <- (1 /(1 + exp(-((mids-SL50[index[i]])/Sslope[index[i]]))))
}
}
if(start_ages==0){
S_fl[,1] <- 1e-5
}
if(any(selex_type=="dome")){
index <- which(selex_type=="dome")
for(i in 1:length(index)){
Sfull <- which(round(S_fl[index[i],],2)==1.00)[1]
if(is.na(Sfull)) Sfull <- which(round(S_fl[index[i],],1)==1.00)[1]
find_dome <- (Sfull+1):length(S_fl[index[i],])
S_fl[index[i],find_dome] <- exp((-(find_dome-Sfull)^2)/(2*dome_sd[index[i]]^2))
}
}
S_fa <- t(sapply(1:nfleets, function(x){
colSums(t(plba_a)*S_fl[x,])
}))
if(start_ages==0){
S_fa[,1] <- 1e-5
}
if(any(selex_type=="dome")==FALSE) Sfull <- NULL
S_fl_out <- data.frame("Variable"="Selectivity", "By"="Length", "X"=c(sapply(1:ncol(S_fl), function(x) rep(mids[x], nfleets))), "Value"=c(S_fl), "Fleet"=rep(1:nfleets, ncol(S_fl)))
W_l_out <- data.frame("Variable"="Weight", "By"="Length", "X"=mids, "Value"=W_l, "Fleet"=0)
Mat_l_out <- data.frame("Variable"="Maturity", "By"="Length", "X"=mids, "Value"=Mat_l, "Fleet"=0)
S_fa_out <- data.frame("Variable"="Selectivity", "By"="Age", "X"=c(sapply(1:ncol(S_fa), function(x) rep(ages[x], nfleets))), "Value"=c(S_fa), "Fleet"=rep(1:nfleets, ncol(S_fa)))
L_a_out <- data.frame("Variable"="Length", "By"="Age", "X"=ages, "Value"=L_a, "Fleet"=0)
W_a_out <- data.frame("Variable"="Weight", "By"="Age", "X"=ages, "Value"=W_a, "Fleet"=0)
Mat_a_out <- data.frame("Variable"="Maturity", "By"="Age", "X"=ages, "Value"=Mat_a, "Fleet"=0)
df <- rbind(S_fl_out, W_l_out, Mat_l_out, S_fa_out, L_a_out, W_a_out, Mat_a_out)
df$Fleet <- as.factor(df$Fleet)
## output list
Outs <- NULL
Outs$df <- df
Outs$vbk <- vbk
Outs$linf <- linf
Outs$t0 <- t0
Outs$binwidth <- binwidth
Outs$CVlen <- CVlen
Outs$SigmaC <- SigmaC
Outs$SigmaI <- SigmaI
Outs$SigmaR <- SigmaR
Outs$SigmaF <- SigmaF
Outs$R0 <- R0
Outs$lwa <- lwa
Outs$lwb <- lwb
Outs$S50 <- S50
Outs$S95 <- S95
Outs$SL50 <- SL50
Outs$SL95 <- SL95
Outs$Sfull <- Sfull
Outs$dome_sd <- dome_sd
Outs$selex_type <- selex_type
Outs$selex_input <- selex_input
Outs$plba <- plba_a
Outs$maturity_input <- maturity_input
Outs$h <- h
Outs$qcoef <- qcoef
Outs$M <- M
Outs$AgeMax <- AgeMax
Outs$ages <- ages
Outs$mids <- mids
Outs$highs <- highs
Outs$lows <- lows
Outs$S_fa <- S_fa
Outs$S_fl <- S_fl
Outs$L_a <- L_a
Outs$W_a <- W_a
Outs$W_l <- W_l
Outs$M50 <- M50
Outs$M95 <- M95
Outs$ML50 <- ML50
Outs$ML95 <- ML95
Outs$Mat_a <- Mat_a
Outs$Mat_l <- Mat_l
Outs$Fequil <- Fequil/nseasons
Outs$Frate <- Frate
Outs$rho <- rho
Outs$theta <- theta
Outs$nseasons <- nseasons
Outs$nfleets <- nfleets
return(Outs)
}
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