This function fits 14 models of recruitment-stock relationships to recruitment numbers and spawning stock (e.g., spawning stock biomass or fecundity) data and provides model selection statistics for determining the best model fit.
sr(recruits = NULL, stock = NULL, model = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14),
select = 1, initial = list(RA = NULL, RB = NULL, Rrho = NULL, BHA = NULL,
BHB = NULL, BHrho = NULL,
SHA = NULL, SHB = NULL, SHC = NULL, DSA = NULL, DSB = NULL, DSC = NULL,
MYA = NULL, MYB = NULL,
MYC = NULL), control = list(maxit = 10000), plot = FALSE)Lists containing estimation results. results contains parameter estimates,
associated standard errors, residual variances, negative log-likelihoods and AICc values for each model.
If the standard errors are NaN, the hessian could not be inverted (i.e., poor model fit).
evidence_ratios contains Akaike weights and evidence ratios for model selection.
convergence contains convergence criterion: 0 = no problems, >0 = problems (see function optim).
correlations contains the estimated parameter correlations. Correlation will be NA if hessian could not be
inverted. predicted contains the predicted values from each model. residuals contains the residuals from each model.
a vector of numbers of recruits
any spawning stock quantity (e.g., spawning biomass, numbers, fecundity) corresponding to the vector of recruits.
the model to fit. Models are 0 = Density-Independent, 1 = Ricker with uncorrelated normal errors (N-U), 2 = Ricker with uncorrelated log-normal errors (L-U), 3 = Ricker with correlated normal errors (N-C), 4 = Ricker with correlated log-normal errors (L-C), 5 = Beverton-Holt with uncorrelated normal errors, 6 = Beverton-Holt with uncorrelated log-normal errors, 7 = Beverton-Holt with correlated normal errors, 8 = Beverton-Holt with correlated log-normal errors, 9 = Shepherd with uncorrelated normal errors, 10 = Shepherd with uncorrelated log-normal errors, 11 = Deriso-Schnute with uncorrelated normal errors, 12 = Deriso-Schnute with uncorrelated log-normal errors, 12 = Myers depensatory model with uncorrelated normal errors, and 14 = Myers depensatory model with uncorrelated log-normal errors. Default is all.
method used to determine starting values. 1 = automatic, 2 = user-specified. Default=1. Automatic selection of starting might not always work given the data provided.
if select = 2, list of starting values for each equation type. See equation parameter names in Details.
see function optim.
logical indicating whether an observed-predicted plot should be produced. Default = FALSE.
Gary A. Nelson, Massachusetts Division of Marine Fisheries gary.nelson@mass.gov
The following equations are fitted:
Ricker: recruits = RA*stock*exp(-RB*stock)
Beverton-Holt: recruits = (BHA*stock)/(1+(BHA*stock)/BHB)
Shepherd: recruits = (SHA*stock)/(1+SHB*stock^SHC)
Deriso-Schnute: recruits = DSA*stock*(1-DSB*DSC*stock)^(1/DSC)
Myers: (MYA*datar$stock^MYC)/(1+((datar$stock^MYC)/MYB))
Maximum likelihood is used to estimate model parameters.
For uncorrelated normal errors, the negative log-likelihood is
n/2*log(2*pi)+n*log(sqrt(sigma2))+1/(2*sigma2)*sum((recruits-predicted)^2)
where n is the number of observation, sigma2 is the maximum likelihood of residual variance and predicted is the model predicted recruits. sigma2 is calculated internally as
sigma2 = sum((recruits-predicted)^2)/n.
For uncorrelated log-normal errors, the negative log-likeliood is
n/2*log(2*pi)+n*log(sqrt(lsigma2))+sum(log(recruits))+1/(2*lsigma2)*
sum((log(recruits)-log(predicted)+lsigma2/2)^2)
lsigma2 is calculated internally as lsigma2 = sum((log(recruits)-log(predicted))^2)/n.
For correlated normal errors, the negative log-likelihood is
n/2*log(2*pi)+n*log(sqrt(sigma2w))-0.5*log(1-rho^2)+
1/(2*sigma2w)*sumR+((1-rho^2)/(2*sigma2w))*(datar$recruits[1]-predicted[1])^2
where rho is the estimated autocorrelation (AR1) parameter, sigma2w is the white noise residual variance, and sumR is calculated as
for(k in 2:n) sumR<-sumR+(recruits[k]-rho*recruits[k-1]-
predicted[k]+rho*predicted[k-1])^2
sigma2w is calculated internally as
res = recruits - predicted
es = c(res[1:c(length(res)-1)]*rho)
sigma2w = sum((res[-1]-es)^2)/c(n-1)
For correlated log-normal errors, the negative log-likelihood is
n/2*log(2*pi)+n*log(sqrt(lsigma2w))+sum(log(recruits))-0.5*log(1-rho^2)+
1/(2*lsigma2w)*lsumR+((1-rho^2)/(2*lsigma2w))*(log(recruits[1])-
log(predicted[1])+lsigma2w/2)^2
where lsumR is calculated as
for(k in 2:n) lsumR<-lsumR+(log(recruits[k])-pho*log(recruits[k-1])
-log(predicted[k])+rho*log(predicted[k-1])+(1-phi)*lsigma2w/2)^2
and lsigma2w is calculated as
res = log(recruits)-log(predicted)
es = c(res[1:c(length(res)-1)]*pho)
lsigma2w = sum((res[-1]-es)^2)/c(n-1).
Correlated error structures are available for the Ricker and Beverton-Holt model only. The
names for specification of starting values of the AR1 parameter are Rrho and BHrho.
Akaike Information Criterion for small sample sizes (AICc), Akaike weights and evidence ratios (Burham and Anderson 2002) are provided for each model selected above.
This function uses function optim to estimate parameters and function hessian in package numDeriv to calculate the hessian matrix from which standard errors are derived.
Brodziak, J, and C. M. Legault. 2005. Model averaging to estimate rebuilding targets for overfished stocks. Canadian Journal of Fisheries and Aquatic Sciences 62: 544-562.
Brodziak, J, and C. M. Legault. 2010. Reference manual for SRFIT version 7. NOAA Fisheries Toolbox.
Burnham, K. P. and D. R. Anderson. 2002. Model Selection and Multimodel Inference, Second edition. Springer-Verlag New York, New York. 488 pages.
Myers, R. A., N. J. Barrowman, J. A. Hutching and A. A. Rosenberg. 1995. Population dynamics of exploited fish stocks at low population levels. Science 269: 1106-1108.
Quinn, T. J. and R. B. Deriso. 1999. Quantitative fish dynamics. Oxford University Press. 542 pages.
if (FALSE) {
data(striper)
outs<-sr(recruits=striper$recruits,stock=striper$stock,select=2,model=c(5,6,7,8),
initial=list(RA=5e3,RB=2e-5,Rrho=0.1,
BHA=8e3,BHB=1e8,BHrho=0.1,
SHA=1.5e3,SHB=5.6e8,SHC=1,
DSA=9e3,DSB=9e-5,DSC=-1.14,
MYA=1e6,MYB=1e5,MYC=0.4),plot=TRUE)
}
Run the code above in your browser using DataLab