Checks and reformats data in preparation for running with runRECA or RunRecaEstimate
Not to be confused with PrepareRecaEstimate, which is primarily intended for including in Stox projects.
Usage
prepRECA(
samples,
landings,
fixedEffects,
randomEffects,
carEffect = NULL,
neighbours = NULL,
nFish = NULL,
ageError = NULL,
minAge = NULL,
maxAge = NULL,
maxLength = NULL,
lengthResolution = NULL,
testMax = 1000,
date = NULL,
month = NULL,
quarter = NULL,
hatchDay = 1,
interaction = NULL
)Arguments
- samples
data.table() with samples, each row corresponding to one sampled fish. Contains columns:
- catchId
Column identifying the catch that the sample was taken from. Typically a haul or a landing.
- sampleId
Column identifying the sample. If only one sample is taken for each catch. This can be set equal to catchId
- date
POSIXct() Date of catch
- Age
integer() Age of fish
- Length
numeric() Length of fish in cm. Must be complete (no NAs)
- Weight
numeric() Weight of fish in kg. Fish with missing values will not be included in Weight-given-length model.
- Otolithtype
integer(), optional, Code identifying stock-classification, may contain integers 1,2,4 and 5.
- ...
Additional columns which may be used as covariates as covariates. Type of covariate must be sepcified in 'fixedEffects', 'randomEffects' or 'carEffect'
- landings
data.table() with total landings, each row corresponding to one cell. Contains columns:
- LiveWeightKG
numeric(). Total landings (Live/Round weight in Kg) for the cell
- ...
Additional columns which may be used as covariates. Covariates in landings define each cell. Type of covariate must be sepcified in 'fixedEffects', 'randomEffects' or 'carEffect'
- fixedEffects
character() vector specifying fixed effects. Corresponding columns must exists in samples and landings.
- randomEffects
character() vector specifying random effects. Corresponding columns must exists samples (may also exist in landings).
- carEffect
character() specifying a random effect with conditional autoregressive coefficient. Corresponding columns must exists samples (may also exist in landings).
- neighbours
list() specifying the neighbourhood-structure for the carEffect. neighbours[a] should provide a vector of neighbours to a. May be NULL of no carEffect is used.
- nFish
data.table() with the columns 'sampleId' and 'count', specifying the number of fish in the part of the catch that each sample was taken from. Not always needed. See details.
- ageError
matrix() specifying the probability of read age (rows), given true age (columns). Row and column names specify the ages. If NULL, a unit matrix is assumed (No error in age reading).
- minAge
lowest age to include in model. If NULL, minimal age in samples is used. Age range must match any age error matrix provided (ageError)
- maxAge
highest age to include in model. If NULL, maximal age in samples is used. Age range must match any age error matrix provided (ageError)
- maxLength
longest length to include in model. If NULL, maximal length in samples is used.
- lengthResolution
desired resolution for length groups. If NULL minimal difference in first testMax records are used.
- testMax
The largest number of record to inspect for deriving lengthResolution.
- date
POSIXct() vector, matching the number of rows in 'landings', date of catch, see details.
- month
integer() vector, matching the number of rows in 'landings', month of catch (1 for January, etc.), see details.
- quarter
integer() vector, vector, matching the number of rows in 'landings', quarter of catch (1 for Q1, etc.), see details.
- hatchDay
integer(), encoding the day of the year when fish is consider to transition from one age to the next.
- interaction
character vector specifying effects that should be included in interaction term. Must correspond to effects specified in parameters 'fixedEffects', 'randomEffects', or 'carEffect'
- sampleID
Column idenitfying the sample, defined as for 'samples'
- count
Estimated number of fish in the part of the catch the sample was taken from
Value
RecaData Data and some data related parameters prepared for running Reca.
Details
The cell definition is specified by 'landings'. The type of covariates are specified in fixedEffects, randomEffects and carEffect. All fixed effects, as well as any car-effect, must be included in the cell definition. All covariates must occur in samples.
The parameters 'date', 'month', and 'quarter' are used to set the temporal resolution for catch at age prediction. Provide exactly one of these, and set the other ones to NULL. Temporal resolution need not match any temporal covariate used. One can for example run with month, even if Quarter is a covariate in the model. Note that resolution is sensitive to data volume. If you get errors in prediction with E_p(a) = nan, consider trying with quarter.
neighbours must be symetric, so that b %in% neighbours[a], implies a %in% neighbours[b]
nfish is only needed when several samples may be taken from the same catch. If these are stratified in any way (e.g. pre-sorting by size or sex), an estimate of strata sizes must be given (column 'count'), for each sample (column 'sampleId'). If these are replicate samples from the same selection frame, an estimate of the total catch may be given.
If the column 'Otolithtype' is provided, data is prepared for running stock-splitting analysis.
Support 4 otolithtypes, where type 1 and 2 correspond to one stock, and type 4 and 5 correspond to another
review documentation for eca.estimate and eca.predict,
for information about how to configure running of this stock splitting, and how to interpret results.
output GlobalParameters: While outputs AgeLength, WeightLength and Landings are complete and ready for R-ECA runs. This function populates the list of GlobalParameters only partially. Run parameters have to be added afterwards.
Examples
data(catchsamples)
catchsamples$catchId <- catchsamples$LEid
catchsamples$sampleId <- catchsamples$SAid
catchsamples$date <- catchsamples$LEdate
catchsamples$Metier5 <- catchsamples$LEmetier5
data(landings)
landings$LiveWeightKG <- landings$OfficialLandingsWeight
landings$Metier5 <- landings$FishingActivityCategoryEuropeanLvl5
# inspect data
rEcaDataReport(catchsamples, landings, c("Metier5", "VDencrCode"))
#> Metier5 LiveWeightKG LiveWeightCumFraction NVDencrCode Ndate Ncatch Nsample
#> 1: OTB_DEF 39822917 0.4175925 1 1 1 1
#> 2: LLS_DEF 26807800 0.6987055 34 16 34 35
#> 3: SSC_DEF 14299890 0.8486575 29 23 29 29
#> 4: LX_DEF 9328848 0.9464820 0 0 0 0
#> 5: GNS_DEF 2754654 0.9753680 17 9 17 17
#> 6: LLD_DEF 2063217 0.9970034 0 0 0 0
#> 7: PTB_DEF 118092 0.9982417 0 0 0 0
#> 8: LHM_DEF 83410 0.9991164 0 0 0 0
#> 9: OTM_DEF 63339 0.9997806 0 0 0 0
#> 10: FPO_DEF 9051 0.9998755 0 0 0 0
#> 11: PS_DEF 5267 0.9999307 0 0 0 0
#> 12: OTT_DEF 5155 0.9999848 0 0 0 0
#> 13: GND_DEF 1202 0.9999974 0 0 0 0
#> 14: MIS_DEF 251 1.0000000 0 0 0 0
#> Nage Nweight Nlength
#> 1: 29 29 29
#> 2: 962 962 962
#> 3: 721 721 721
#> 4: 0 0 0
#> 5: 338 338 338
#> 6: 0 0 0
#> 7: 0 0 0
#> 8: 0 0 0
#> 9: 0 0 0
#> 10: 0 0 0
#> 11: 0 0 0
#> 12: 0 0 0
#> 13: 0 0 0
#> 14: 0 0 0
# define sampling frame
landings <- landings[landings$Metier5 %in% c("GNS_DEF", "LLS_DEF", "LX_DEF", "SSC_DEF"),]
landings <- landings[landings$Area %in% c("27.2.a.2", "27.1.b"),]
catchsamples <- catchsamples[catchsamples$Metier5 != "OTB_DEF",]
# merge gear groups
landings[landings$Metier5 == "LX_DEF", "Metier5"] <- "LSS_LX_DEF"
landings[landings$Metier5 == "LLS_DEF", "Metier5"] <- "LSS_LX_DEF"
catchsamples[catchsamples$Metier5 == "LX_DEF", "Metier5"] <- "LSS_LX_DEF"
catchsamples[catchsamples$Metier5 == "LLS_DEF", "Metier5"] <- "LSS_LX_DEF"
# inspect data
rEcaDataReport(catchsamples, landings, c("Metier5", "VDencrCode"))
#> Metier5 LiveWeightKG LiveWeightCumFraction NVDencrCode Ndate Ncatch
#> 1: LSS_LX_DEF 31558580 0.7190532 34 16 34
#> 2: SSC_DEF 9827412 0.9429679 29 23 29
#> 3: GNS_DEF 2503085 1.0000000 17 9 17
#> Nsample Nage Nweight Nlength
#> 1: 35 962 962 962
#> 2: 29 721 721 721
#> 3: 17 338 338 338
#attempt prepRECA, gives error
if (FALSE) prepRECA(catchsamples,
landings,
c("Metier5"),
NULL,
quarter = landings$Quarter)
#get catch count estimates
meanWeights <- stats::aggregate(list(meanW=catchsamples$Weight),
by=list(sampleId=catchsamples$sampleId),
FUN=mean)
#total weight is unique for sampleID, hence the FUN=mean
totalWeights <- stats::aggregate(list(totalW=catchsamples$SAtotalWtLive),
by=list(sampleId=catchsamples$sampleId),
FUN=mean)
nFish <- merge(totalWeights, meanWeights)
nFish$count <- nFish$totalW / nFish$meanW
nFish <- nFish[,c("sampleId", "count")]
#prepRECA (produce recaData as in data(recaDataExample))
recaDataExample <- prepRECA(catchsamples,
landings,
c("Metier5"),
NULL,
nFish = nFish,
quarter = landings$Quarter)