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Create expected catch/expected revenue matrix

Source: R/create_expectations.R
create_expectations.Rd

Create expected catch or expected revenue matrix. The matrix is required for the logit_c model. Multiple user-defined matrices can be saved by setting replace.output = FALSE and re-running the function.

Usage

create_expectations(
  dat,
  project,
  catch,
  price = NULL,
  defineGroup = NULL,
  temp.var = NULL,
  temporal = "daily",
  calc.method = "standardAverage",
  lag.method = "simple",
  empty.catch = NULL,
  empty.expectation = 1e-04,
  temp.window = 7,
  temp.lag = 0,
  year.lag = 0,
  dummy.exp = FALSE,
  default.exp = FALSE,
  replace.output = TRUE,
  weight_avg = FALSE,
  outsample = FALSE
)

Arguments

dat

Primary data containing information on hauls or trips. Table in FishSET database contains the string 'MainDataTable'.

project

String, name of project.

catch

Variable from dat containing catch data.

price

Optional, variable from dat containing price/value data. Price is multiplied against catch to generated revenue. If revenue exists in dat and you wish to use this revenue instead of price, then catch must be a vector of 1 of length equal to dat. Defaults to NULL.

defineGroup

Optional, variable from dat that defines how to split the fleet. Defaults to treating entire dataframe dat as a fleet.

temp.var

Optional, temporal variable from dat. Set to NULL if temporal patterns in catch should not be considered.

temporal

String, choices are "daily" or "sequential". Should time, if temp.var is defined, be included as a daily timeline or sequential order of recorded dates. For daily, catch on dates with no record are filled with NA. The choice affects how the rolling average is calculated. If temporal is daily then the window size for average and the temporal lag are in days. If sequential, then averaging will occur over the specified number of observations, regardless of how many days they represent.

calc.method

String, how catch values are average over window size. Select standard average ("standardAverage"), simple lag regression of means ("simpleLag"), or weights of regressed groups ("weights")

lag.method

String, use regression over entire group ("simple") or for grouped time periods ("grouped").

empty.catch

String, replace empty catch with NA, 0, mean of all catch ("allCatch"), or mean of grouped catch ("groupCatch").

empty.expectation

Numeric, how to treat empty expectation values. Choices are to not replace (NULL) or replace with 0.0001 or 0.

temp.window

Numeric, temporal window size. If temp.var is not NULL, set the window size to average catch over. Defaults to 14 (14 days if temporal is "daily").

temp.lag

Numeric, temporal lag time. If temp.var is not NULL, how far back to lag temp.window.

year.lag

If expected catch should be based on catch from previous year(s), set year.lag to the number of years to go back.

dummy.exp

Logical, should a dummy variable be created? If TRUE, output dummy variable for originally missing value. If FALSE, no dummy variable is outputted. Defaults to FALSE.

default.exp

Whether to run default expectations. Defaults to FALSE. Alternatively, a character string containing the names of default expectations to run can be entered. Options include "recent", "older", "oldest", and "logbook". The logbook expectation is only run if defineGroup is used. "recent" will not include defineGroup. Setting default.exp = TRUE will include all four options. See Details for how default expectations are defined.

replace.output

Logical, replace existing saved expected catch data frame with new expected catch data frame? If FALSE, new expected catch data frames appended to previously saved expected catch data frames. Default is TRUE. If TRUE

weight_avg

Logical, if TRUE then all observations for a given zone on a given date will be included when calculating the mean, thus giving more weight to days with more observations in a given zone. If FALSE, then the daily mean for a zone will be calculated prior to calculating the mean across the time window.

outsample

Logical, if TRUE then generate expected catch matrix for out-of-sample data. If FALSE generate for main data table. Defaults to outsample = FALSE

Value

Function saves a list of expected catch matrices to the FishSET database as projectExpectedCatch. The list includes the expected catch matrix from the user-defined choices, recent fine grained information, older fine grained information, oldest fine grained information, and logbook level information. Additional expected catch cases can be added to the list by specifying replace.output = FALSE. The list is automatically saved to the FishSET database and is called in make_model_design. The expected catch output does not need to be loaded when defining or running the model.

newGridVar, newDumV

Details

Function creates an expectation of catch or revenue for alternative fishing zones (zones where they could have fished but did not). The output is saved to the FishSET database and called by the make_model_design function. create_alternative_choice must be called first as observed catch and zone inclusion requirements are defined there.
The primary choices are whether to treat data as a fleet or to group the data (defineGroup) and the time frame of catch data for calculating expected catch. Catch is averaged along a daily or sequential timeline (temporal) using a rolling average. temp.window and temp.lag determine the window size and temporal lag of the window for averaging. Use temp_obs_table before using this function to assess the availability of data for the desired temporal moving window size. Sparse data is not suited for shorter moving window sizes. For very sparse data, consider setting temp.var to NULL and excluding temporal patterns in catch.
Empty catch values are considered to be times of no fishing activity. Values of 0 in the catch variable are considered times when fishing activity occurred but with no catch. These points are included in the averaging and dummy creation as points in time when fishing occurred.
Four default expected catch cases will be run:

  • recent: Moving window size of two days. In this case, there is no grouping, and catch for entire fleet is used.

  • older: Moving window size of seven days and lag of two days. In this case, vessels are grouped (or not) based on defineGroup argument.

  • oldest: Moving window of seven days and lag of eight days. In this case, vessels are grouped (or not) based on defineGroup argument.

  • logbook: Moving window size of 14 days and lag of one year, seven days. Only used if fleet is defined in defineGroup.

Examples

if (FALSE) {
create_expectations(pollockMainDataTable, "pollock", "OFFICIAL_TOTAL_CATCH_MT",
  price = NULL, defineGroup = "fleet", temp.var = "DATE_FISHING_BEGAN",
  temporal = "daily", calc.method = "standardAverage", lag.method = "simple",
  empty.catch = "allCatch", empty.expectation = 0.0001, temp.window = 4,
  temp.lag = 2, year.lag = 0, dummy.exp = FALSE, replace.output = FALSE,
  weight_avg = FALSE, outsample = FALSE
)
}