fcs2EQRSummaryMatrix: EQR Summary Matrix
In aquaMetrics/fcs2: Fisheries Classification Scheme 2 For SNIFFER

Description Usage Arguments Value See Also

Produces a matrix summarising the single and joint EQRs and optionally comparing these to the observed catches and the FCS2 model's predictions.

fcs2EQRSummaryMatrix(
  fit1,
  ...,
  newData,
  joinByVar = NULL,
  subset = 1:nrow(newData),
  na.action,
  classify = !missing(boundaries),
  boundaries,
  summary = "mean",
  observations = TRUE,
  predictions = TRUE,
  n.samples = 1000,
  n.sims = 1000,
  eqrs,
  seed = NULL
)

`fit1`	an `"fcs2Fit"` object containing a full FCS2 model fit, as returned from `fcs2FitModel` with `runBUGS = TRUE`.
`...`	further `"fcs2Fit"` objects, each resulting from a full FCS2 model fit with a different species.
`newData`	a data frame with surveys as rows and variables as columns. It should contain all variables required by each of the model fits. Covariates which are related to human disturbance (pressure variables) should have their values set to the value expected at the site if it were undisturbed (reference conditions) rather than the observed value for each of these variables.
`joinByVar`	the name of a column in `dataFrame` to be used for joining multiple surveys together when calculating the EQR. For example, `joinByVar = "WBId"` would produce EQR samples for each water body, with every survey in a water body contributing towards its EQR. If `NULL` (the default), EQRs are calculated for each survey only.
`subset`	an optional vector specifying a subset of surveys to calculate EQR 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. 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.
`classify`	whether to produce probabilistic classifications of each WFD class. If `joinByVar = NULL` (the default), classifications are made using the joint EQR for each survey. If `joinByVar` is provided, classifications are made using the combined EQRs across multiple surveys.
`boundaries`	a vector of length 4 giving the EQR boundaries separating the classes Bad, Poor, Good, Moderate and High. This is used if `classify = TRUE` to produce probabilistic classifications of each class. If missing, regularly spaced boundaries of `c(0.2, 0.4, 0.6, 0.8)` are used with a warning.
`summary`	a character vector listing how to summarise the EQR variables. Can contain `"mean"` for the mean and/or `"sd"` to calculate the standard deviation.
`observations`	whether to give the observed total catch for each survey and species.
`predictions`	whether to summarise the model's predicted total catch for each survey and species. If `TRUE` (the default), the expected total catch is given but if `"detail"`, the probability of presence (prevalence) and the expected total catch if present are alternatively given. If `"all"`, all three columns are shown for each species.
`n.samples`	the number of Monte Carlo EQR samples to produce for each survey (or joining variable).
`n.sims`	the number of Monte Carlo simulations to make for each EQR sample. These internal samples are used for approximating the probability that defines the joint EQR.
`eqrs`	the result of `fcs2JointAndSingleEQR` with the above arguments can alternatively be given, to save recalculation if available.
`seed`	set random seed to allow repeatable results.