Description Usage Arguments Value Note See Also Examples
Estimate sigma R (recruitment variability), based on the empirical standard deviation of recruitment deviates in log space.
1  estSigmaR(model, digits=2)

model 
fitted 
digits 
number of decimal places to use when rounding, or

Vector of two numbers, estimating recruitment variability based on (1) the estimated age composition in the first year, and (2) subsequent annual recruitment.
This function uses the empirical standard deviation to estimate sigma R, which may be appropriate as likelihood penalty (or Bayesian prior distribution) for recruitment deviates from the stockrecruitment curve. The smaller the estimated recruitment deviates, the smaller the estimated sigma R.
estSigmaR
can be used iteratively, along with
estN
and estSigmaI
to assign likelihood
weights that are indicated by the model fit to the data. Sigmas and
sample sizes are then adjusted between model runs, until they
converge. The iterate
function facilitates this procedure.
If ss is the sum of squared recruitment deviates in log space and n is the number of estimated recruitment deviates, then the estimated sigma R is:
sigmaR = sqrt(ss/n)
The denominator is neither n1 nor np, since ss is based on deviates from zero and not the mean, and the deviates do not converge to zero as the number of model parameters increases.
getN
, getSigmaI
, getSigmaR
,
estN
, estSigmaI
, and estSigmaR
extract and estimate sample sizes and sigmas.
iterate
combines all the get*
and est*
functions in one call.
plotN
and plotB(..., what="s")
show what
is behind the sigma R estimation.
scapepackage
gives an overview of the package.
1 2 3 4 5 6 7 8 9 10 11  getSigmaR(x.cod) # sigmaR used in assessment 0.5 and 1.0
estSigmaR(x.cod) # model estimates imply 0.20 and 0.52
getSigmaR(x.ling) # 0.6, deterministic age distribution in first year
estSigmaR(x.ling) # model estimates imply 0.36
getSigmaR(x.sbw)
estSigmaR(x.sbw) # large deviates in first year
plotN(x.sbw) # enormous plus group and 1991 cohort
# x.oreo assessment had deterministic recruitment, so no deviates

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