dCatch: Probabilities of the total catch distribution
In aquaMetrics/fcs2: Fisheries Classification Scheme 2 For SNIFFER

Description Usage Arguments Value See Also

Density and distribution function of the predicted total catch over all passes.

1	dCatch(fit, x, newData, subset = 1:nrow(newData), na.action, mu, rho)

`fit`	an `"fcs2Fit"` object containing a full FCS2 model fit, as returned from `fcs2FitModel` with `runBUGS = TRUE`.
`x`	vector of (non-negative integer) quantiles.
`newData`	a data frame with surveys as rows and variables as columns. It should contain all variables required by `fit`.
`subset`	an optional vector specifying a subset of surveys to calculate the predicted total catch for.
`na.action`	a function which indicates what should happen when the data contain missing values (`NA`s). The default is set by the `na.action` setting of `options` and this is usually set to `na.omit`. This setting removes surveys that contain missing data in any required variables. A vector indicating the rows that were removed can be extracted from the returned object using `na.action`. Alternatively, `na.pass` can be used to ignore missing values (where possible) or `na.fail` can be given to signal an error if missing values are found.
`mu`	a matrix of posterior samples of the abundance component mu can optionally be given to save recalculation if already available. This is assumed to have been calculated from `abundance` using the same arguments as above.
`rho`	a matrix of posterior samples of the prevalence component rho can optionally be given to save recalculation if already available. This is assumed to have been calculated from `prevalence` using the same arguments as above.

dCatch gives the probability mass function and pCatch the probability distribution function evaluated at values x for the selected surveys. In each case, a matrix is returned with rows for x values and columns for selected surveys.

fcs2FitModel for fitting the FCS2 model.
predict.fcs2Fit for calculating the expected total catch.
plotCatchPMF for plotting these probabilities.
dzinbinom for the zero-inflated negative binomial distribution, which these functions use.