In laurieKell/biodyn: Biomass Dynamic Model

library(knitr)

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## Global options
options(max.print="75")
opts_chunk$set(fig.path="out/",
               echo =TRUE,
               eval=TRUE,
               cache=!TRUE,
               cache.path="cache/",
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opts_knit$set(width=75)

Introduction

The jack knife can be used to estimate standard errors and bias for parameter in non-linear estimation and quantities derived from theme.

Estimation

When using the jack knife the $i^{th}$ observation is omitted in turn from the sample data set and the parameter re-estimated and its mean calculated $\overline{\theta}_i$.

$$f(X)\simeq\overline{f^??}\equiv\frac{1}{N}\sum_{i=1}^{N}f_i^J$$

Variance

The variance can is then calculated by

$$\sigma_{f(\bar{x})} = \sqrt{N-1}\sigma_{f^J}$$

where

$$ \sigma^2_{f^J} \equiv \overline{(f^J)^2} -(\overline{f^J)}^2$$

The covariance between two parameters $\alpha$ and $\beta$ parameters is given by

$$ COV_{\alpha,\beta}=\frac{N-1}{N}\sum_{i=1}^N [\theta_{i}^J-\overline{\theta^J}]a [\theta{i}^J-\overline{\theta^J}]_b$$

where $$\overline{\theta_\alpha^J}=\frac{1}{N}\sum{i=1}{N} \theta_J^{i,\alpha}$$

$\textstyle N-1$ is used as the denominator rather than $\textstyle N$ since the population mean is unknown.

Bias estimation and correction

The bias of the estimator, calculated over the entire sample, can be estimated by

$$f(X)\simeq N f(\bar{x})-(N-1)\overline{f^J}$$

and reduces bias by an order of magnitude from $O(N^{-1})$ to $O(N^{-2})$. \newpage

Simulation

We simulate an object with known properties using the biodyn package then conduct an assessment.

library(plyr)
library(biodyn)
library(ggplot2)
library(ggplotFL)

bd=sim()
bd=window(bd,end=49)

A proxy for stock abundance is also required to estimate the stock parameters, Therefore an unbiased catch per effort (CPUE) series is generated from mid year stock biomass.

cpue=(stock(bd)[,-dims(bd)$year]+stock(bd)[,-1])/2
cpue=rlnorm(1,log(cpue),.2)

ggplot(cpue)+geom_point(aes(year,data))

Before performing an assessment we have to provide initial guesses and bounds for the parameters

#set parameters
setParams(bd) =cpue
setControl(bd)=params(bd)
control(bd)[3:4,"phase"]=-1

The stock assessment can now be fitted

#fit
bdHat=fit(bd,cpue)

plot(biodyns("Estimate"=bdHat,"Actual"=bd))+
  theme(legend.position="bottom")

\newpage

Jack knife procedure

The CPUE index is jack knifed and the model refitted for each resampled data set, which are held in the iter dim.

bdJK=fit(bdHat,jackknife(cpue))

The normal plot method can not be used as the iter does not hold samples from a probability distribution. Instead plotJack is used which estimates the error bars.

plotJack(bdHat,bdJK)

Estimates

Estimates of the bias and SEs can now be calculated, these can be done for FLQuant and FLPar objects, e.g. for current stock biomass

#FLQuant
library(ggplotFL)

true=stock(bd   )[,49]
hat =stock(bdHat)[,49]
jack=stock(bdJK )[,49]


n   =dims(jack)$iter
mn  =apply(jack, 1:5, mean)   
rsdl=sweep(jack,1:5,mn,"-")
ss  =apply(rsdl^2,1:5,sum)

bias         =(n-1)*(hat-mn)
biasCorrected=n*hat-(n-1)*mn

se  =sqrt(((n-1)/n)*ss)

Reference points

# FLPar
true=refpts(bd   )
hat =refpts(bdHat)
jack=refpts(bdJK )

n   =dims(jack)$iter
mn  =apply(jack,  seq(length(dim(jack))-1), mean)   
rsdl=sweep(jack,  seq(length(dim(jack))-1), mn,"-")
ss  =apply(rsdl^2,seq(length(dim(jack))-1),sum)

bias         =(n-1)*(hat-mn)
biasCorrected=n*hat-(n-1)*mn

se  =sqrt(((n-1)/n)*ss)