abundance: Posterior Samples of Abundance and Prevalence Model...
In aquaMetrics/fcs2: Fisheries Classification Scheme 2 For SNIFFER

Calculates posterior samples of the abundance or prevalence model components

abundance(fit, newData, subset = 1:nrow(newData), na.action, breakdown = FALSE)

prevalence(fit, newData, subset = 1:nrow(newData), na.action, breakdown = FALSE)

prevalence(
  fit,
  newData,
  subset = 1:nrow(newData),
  na.action,
  breakdown = FALSE
)

`fit`	an `"fcs2Fit"` object containing a full FCS2 model fit, as returned from `fcs2FitModel` with `runBUGS = TRUE`.
`newData`	a data frame with surveys as rows and variables as columns. It should contain all variables required by `fit`. If missing, the model matrix contained within `fit` is used.
`subset`	an optional vector specifying a subset of surveys to calculate samples 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.
`breakdown`	logical; If `FALSE` (the default), samples of the abundance or prevalence are returned for each survey. If `TRUE`, an array is returned contining each additive term in the abundance or prevalence regression equation. If `breakdown = FALSE` (the default), a matrix of posterior samples of the abundance μ or prevalence ρ model component is returned for the selected surveys. Monte Carlo samples appear as rows and surveys as columns. If `breakdown = TRUE`, a three-dimensional array of regression component samples is returned. Samples are given in the first dimension, surveys in the second and the third dimension corresponds to each additive term in the abundance or prevalence regression. The output from `breakdown = FALSE` can be calculated by summing over the third dimension and transforming with `exp` for abundance or `expit` for prevalence. If surveys were removed by `na.action = na.omit`, these can be recovered by applying `na.action` to the matrix or array.