estSigmaR: Estimate Recruitment Sigma In scape: Statistical Catch-at-Age Plotting Environment

Description

Estimate sigma R (recruitment variability), based on the empirical standard deviation of recruitment deviates in log space.

Usage

 `1` ```estSigmaR(model, digits=2) ```

Arguments

 `model` fitted `scape` model containing element `Dev`. `digits` number of decimal places to use when rounding, or `NULL` to suppress rounding.

Value

Vector of two numbers, estimating recruitment variability based on (1) the estimated age composition in the first year, and (2) subsequent annual recruitment.

Note

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 `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 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.

`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.

`scape-package` gives an overview of the package.

Examples

 ``` 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 ```

scape documentation built on May 2, 2018, 1:04 a.m.