Batch fecundity (number of eggs by female fish weight) is estimated with an allometric error structure allowing greater variance with increasing weight. This is estimated as a 4 parameter model where specified parameters can be fixed. If fixed parameters are specified, then the modeling becomes a two-step process where all four parameters are estimated and the resulting parameters estimates are then assigned to the fixed variables in a second phase of the model.
Estimate_Batch_Fecundity( data, start_pars, prediction.int = NULL, return.parameters = FALSE, fixed.pars = NULL, verbose = FALSE )
| data | A dataframe with 2 numeric variables: Fish weight and fecundity (number of eggs). Names and order are not important as the larger of the two variables is assumed to be number of eggs and is automatically assigned as this. |
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| start_pars | A list of 4 start_pars tha must include: "alpha", "beta", "Sigma0" and "Sigma1" |
| prediction.int | A numeric vector of weights to predict batch fecundity over. If being used in conjunction with `Estimate_proportion_female`, then the prediction intervals should be the midpoints of the weight bins returned from that function |
| return.parameters | If TRUE, parameter estimates are returned instead of estimates |
| fixed.pars | A character vector of any start_pars that require fixing. |
| verbose | If TRUE, parameter estimates are printed to the screen |
If a prediction interval is provided then predicted fecundity-at-weight is provided at those intervals with their variance and standard error. If no prediction interval is provided then predictions for the raw data are returned with standard error and 95 are returned.