Estimate sigma R (recruitment variability), based on the empirical standard deviation of recruitment deviates in log space.
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 stock-recruitment curve. The smaller the estimated recruitment deviates, the smaller the estimated sigma R.
estSigmaR can be used iteratively, along with
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
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 n-1 nor n-p, 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.
scape-package gives an overview of the package.
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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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