as.CatDynData: Data Object for the Estimation of Catch Dynamic Models
In CatDyn: Fishery Stock Assessment by Catch Dynamics Models

Description Usage Arguments Details Value Note Author(s) References Examples

It takes the vectors of catch, effort, and mean body weight from a dataframe and creates an object of class CatDynData. Objects of this class are lists with two components, one for properties of the data such as units and another for the data: catch, effort, mean body weight by fleet, and the catch spike statistic.

1 2	as.CatDynData(x, step, fleet.name, coleff, colcat, colmbw, unitseff, unitscat, unitsmbw, nmult, season.dates)

`x`	A dataframe where to find the columns of catch, effort, and mean body weight
`step`	Character. The time step of the dynamics, either "day", "week", or "month".
`fleet.name`	Character. The name of the fleet(s).
`coleff`	Integer. The column(s) in "x" where to find the effort data.
`colcat`	Integer. The column(s) in "x" where to find the catch data.
`colmbw`	Integer. The column(s) in "x" where to find the mean body weight data.
`unitseff`	Character. The unit(s) of effort.
`unitscat`	Character. The unit of catch. Either "ton" (metric tonnes), "kg", or "ind" (individuals).
`unitsmbw`	Character. The unit of body weight. Either "kg", "g", or "ind" (individuals).
`nmult`	Character. The multiplier that scales the catch in numbers. Either "bill" (billions), "mill" (millions), "thou" (thousands), or "ind" (individuals).
`season.dates`	Character vector. A two component character vector with the initial and final dates of the season in the ISO 8601 standard.

The time step determines the rows of x. Make it sure that the number of rows, i.e. the length of the season in time steps, is large enough to estimate all parameters in the model. The simplest model has five parameters, the most complex model has 50 parameters. A rule of thumb would be that the number of time steps be at least three times the number of parameters.

If it is a two fleet system, combine the fleet names, such as c("industrial", "artisanal"), and likewise with coleff, colcat, and colmbw, such as c(5,9) to indicate the columns of catch for the industrial and artisanal fleets respectively. The same applies to units of effort. In a two fleet system, the time step, and the units of catch, mean body weight, and the multiplier must be the same for both fleets.

When the unit of catch and of body weight is "ind", it means that the catch was counted in numbers, not in biomass. In that case the mandatory column of mean body weight should be a column of 1s. The multiplier is the quantity by which the catch in the model shall be raised to be scaled to the actual catch. The idea here is that in many fisheries the daily, weekly, or monthly catch (for example, anchovies, squids) is very large so by setting the multiplier to "bill", "mill", or "thou", the model is working with catches in the orders of tens at most. If the multiplier is set to "ind" then the catch is modeled at the level of the actual catch by time step. This option is useful for sport fisheries, in combination with the poisson or negbin option for distribution.

The season.dates parameter will allow counting the number of steps in integer sequential fashion. If the "time.step" parameter is "day" or "week" the dates may jump one year at most, whereas if the time.step parameter is "month" then season.dates may jump over many years. When the time step is week or months, this parameter needs not be precisely specified; any day within the right week or month will suffice. If you get an error message saying that the number of time steps is not right and that you should consider changing season.dates, then just change the dates a few days until you no longer get the error message.

The catch spike statistic is a fleet-specific statistic that is useful to identify the timing of perturbations to depletion; it is defined as

S_{f,t} = 10 \times (X_{f,t}/max(X_{f,t}) - E_{f,t}/max(E_{f,t}))

where X is the observed catch at time step t by fleet f, and E is the observed effort. When this statistic is positive and high then the time step at which this happened is a candidate for a perturbation. In transit fisheries the complementary reasoning is valid: when the statistic is negative and low then the time step at which this happened is a candidate for the timing of one emmigration event.

A list of length 2.

`Properties`	The units for time step, catch, body weight, and the catch numbers multiplier; the names of the fleets and the units of effort, and the start date and end date of the fishing season
`Data`	One dataframe for each fleet with the time step, effort, catch in biomass, mean body weight, catch in the numbers multiplier, and the catch spike statistic

The objects created with as.CatDynData will pass the raw data to plotting and estimating functions.

Ruben H. Roa-Ureta (ORCID ID 0000-0002-9620-5224)

Roa-Ureta, R. H. 2012. ICES Journal of Marine Science 69(8), 1403-1415.

Roa-Ureta, R. H. et al. 2015. Fisheries Research 171 (Special Issue), 59-67.

Roa-Ureta, R. H. 2015. Fisheries Research 171 (Special Issue), 68-77.

lgahi <- as.CatDynData(x=lolgahi,
                       step="day",
                       fleet.name="Fk",
                       coleff=2,
                       colcat=1,
                       colmbw=3,
                       unitseff="nboats",
                       unitscat="kg",
                       unitsmbw="kg",
                       nmult="bill",
                       season.dates=c(as.Date("1990-01-31"),
                                      as.Date("1990-05-30")))

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 Fishery Stock Assessment by Catch Dynamics Models
 CatDyn version 1.1-1 (built on 2018-12-18) is now loaded
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