R/StoxDataTypes.R
In StoXProject/RstoxFDA: Fisheries Dependent Analysis with RstoX
Defines functions
is.StockSplittingParameters
is.AgeErrorMatrix
is.CarNeighbours
is.AreaPosition
is.TemporalDefinition
is.RecaResult
is.RecaPrediction
is.RecaCatchAtAge
is.RecaParameterData
is.RecaData
is.ReportFdaSamplingData
is.WeightConversionTable
is.LengthConversionTable
is.Translation
is.ParameterConvergenceData
is.ParameterizationSummaryData
is.ReportFdaSOP
is.ReportFdaData
is.StoxBioticData
is.MetierTable
is.IndividualSamplingParametersData
is.PSUSamplingParametersData
is.AnalyticalPopulationEstimateData
is.AnalyticalPSUEstimateData
is.Date
is.POSIXct
Documented in
is.AgeErrorMatrix
is.AreaPosition
is.CarNeighbours
is.LengthConversionTable
is.ParameterConvergenceData
is.ParameterizationSummaryData
is.RecaCatchAtAge
is.RecaData
is.RecaParameterData
is.RecaResult
is.ReportFdaData
is.ReportFdaSamplingData
is.ReportFdaSOP
is.StockSplittingParameters
is.TemporalDefinition
is.Translation
is.WeightConversionTable
#' @noRd
is.POSIXct <- function(date){
if ("POSIXct" %in% class(date)){
return(TRUE)
}
return(FALSE)
}
#' @noRd
is.Date <- function(date){
if ("Date" %in% class(date)){
return(TRUE)
}
return(FALSE)
}
#' Analytical PSU Estimate Data
#'
#' @description
#' Analytical estimates for each PSU
#'
#' List containing the following \code{\link[data.table]{data.table}}s:
#'
#' Abundance
#' \describe{
#' \item{SampleId}{Identfier for Primary sampling unit (PSU)}
#' \item{Stratum}{Identifier for stratification of individuals}
#' \item{Domain}{Identifier of domains for individuals}
#' \item{Abundance}{Total number in Stratum and Domain at th PSU. Unsampled strata is reported as NA}
#' \item{Frequency}{Frequency in Domain within Stratum. Unsampled strata is reported as NA}
#' }
#'
#' Variables
#' \describe{
#' \item{SampleId}{Identfier for Primary sampling unit (PSU)}
#' \item{Stratum}{Identifier for stratification of individuals}
#' \item{Domain}{Identifier of domains for individuals}
#' \item{Variable}{Variable that total and mean is reported for}
#' \item{Total}{Total value of variable in Stratum and Domain at the PSU. Unsampled strata is reported as NA}
#' \item{Mean}{Mean value of variable in Stratum and Domain at the PSU. Unsampled strata is reported as NA}
#' }
#'
#' DomainVariables
#' \describe{
#' \item{Domain}{Identifier of domains for individuals. In addition the domain is identified by the combination of any additional columns in this table}
#' \item{<DomainVariables>}{Columns that relate the domains to data records.}
#' }
#'
#' PSUDomainVariables
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{PSUDomain}{Identfier of domains for PSUs. In addition PSU-domains are identified by the combination of any additional columns in this table.}
#' \item{<DomainVariables>}{Columns that relate the PSU domains to data records.}
#' }
#'
#' StratificationVariables
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{Stratum}{Identfier of stratum for individuals at PSU. In addition strata are identified by the combination of any additional columns in this table.}
#' \item{<StratificationVariables>}{Columns that relate the PSU domains to data records.}
#' }
#'
#' The combination of the columns 'Stratum' and 'SampleId' uniquely identifies a row in the table 'StratificationVariables'
#' The columns <StratificationVariables> are optional, but if present; their combination must identify a stratum for each 'SamplingId'.
#'
#' SampleCount
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{Stratum}{Identfier of stratum for individuals at PSU. In addition strata are identified by the combination of any additional columns in this table.}
#' \item{Domain}{Identifier of domains for individuals. In addition the domain is identified by the combination of any additional columns in this table}
#' \item{nIndividuals}{Number of individuals observed for the domain}
#' }
#'
#' @name AnalyticalPSUEstimateData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if input is correctly formatted Analytical PSU Estimate Data
#' @param table \code{\link[RstoxFDA]{AnalyticalPSUEstimateData}}
#' @return validity
#' @concept Data types
#' @noRd
is.AnalyticalPSUEstimateData <- function(AnalyticalPSUEstimateData){
if (!is.list(AnalyticalPSUEstimateData)){
return(FALSE)
}
if (!all(c("Abundance", "Variables", "DomainVariables", "PSUDomainVariables", "StratificationVariables", "SampleCount") %in% names(AnalyticalPSUEstimateData))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "Abundance", "Frequency") %in% names(AnalyticalPSUEstimateData$Abundance))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "Variable", "Total", "Mean") %in% names(AnalyticalPSUEstimateData$Variables))){
return(FALSE)
}
if (!all(c("Domain") %in% names(AnalyticalPSUEstimateData$DomainVariables))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "nIndividuals") %in% names(AnalyticalPSUEstimateData$SampleCount))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum") %in% names(AnalyticalPSUEstimateData$StratificationVariables))){
return(FALSE)
}
if (any(!(names(AnalyticalPSUEstimateData$StratificationVariables) %in% c("Stratum", "SampleId")))){
stratavars <- names(AnalyticalPSUEstimateData$StratificationVariables)[!(names(AnalyticalPSUEstimateData$StratificationVariables) %in% c("Stratum", "SampleId"))]
stratacount <- AnalyticalPSUEstimateData$StratificationVariables[,list(stratacount=length(unique(paste(get("Stratum"))))), by=list(stratavars=apply(AnalyticalPSUEstimateData$StratificationVariables[,.SD,.SDcol=c(stratavars, "SampleId")], 1, paste, collapse="/"))]
if (any(stratacount$stratacount>1)){
return(FALSE)
}
if (any(is.na(AnalyticalPSUEstimateData$StratificationVariables[,.SD,.SDcols = stratavars]))){
return(FALSE)
}
}
if (any(duplicated(paste(AnalyticalPSUEstimateData$StratificationVariables$SampleId, AnalyticalPSUEstimateData$StratificationVariables$Stratum)))){
return(FALSE)
}
return(TRUE)
}
#' Analytical Population Estimate Data
#'
#' @description
#' Analytical estimates for a population
#'
#' Abundance
#' \describe{
#' \item{Stratum}{Stratum that abundance is provided for.}
#' \item{Domain}{The domain that the abundance is provided for.}
#' \item{Abundance}{The estimated number of individuals in the domain and stratum.}
#' \item{Frequency}{The estimated fraction of individuals in the domain, relative ot the total number in stratum.}
#' }
#'
#' Variables
#' \describe{
#' \item{Stratum}{The stratum that estimates are provided for.}
#' \item{Domain}{The domain that estimates are provided for.}
#' \item{Variable}{The variable (measurment) that estimates are provided for.}
#' \item{Total}{The estimated total value of the variable in domain and stratum.}
#' \item{Mean}{The estimated mean value fo the variable in domain and stratum.}
#' }
#'
#' AbundanceCovariance
#' \describe{
#' \item{Stratum}{The stratum that covariances are provided for.}
#' \item{Domain1}{A domain that covariances are provided for. Unique, given Stratum}
#' \item{Domain2}{A domain that covariances are provided for. Unique, given Stratum and Domain 1.}
#' \item{AbundanceCovariance}{The estimated covariance of abundance between Domain1 and Domain2.}
#' \item{FrequencyCovariance}{The estimated covariance of frequency between Domain1 and Domain2.}
#' }
#'
#' VariableCovariance
#' \describe{
#' \item{Stratum}{The stratum that covariances are provied for.}
#' \item{Domain1}{A domain that covariances are provided for. Unique, given Stratum}
#' \item{Domain2}{A domain that covariances are provided for. Unique, given Stratum and Domain 1.}
#' \item{Variable1}{A variable that covariances are provided for. Unique, given Stratum and domains.}
#' \item{Variable2}{A variable that covariances are provided for. Unique, given Stratum, domains and Variable1.}
#' \item{TotalCovariance}{The estimated covariance of total value of Variable1 in Domain1 and Variable2 in Domain2}
#' \item{MeanCovariance}{The estimated covariance of the mean value of Variable1 in Domain1 and Variable2 in Domain2}
#' }
#'
#' StratificationVariables
#' \describe{
#' \item{Stratum}{A stratum, as identified in other tables.}
#' \item{<StratificationVariables>}{Columns that relate the stratum to data records.}
#' }
#' The columns <StratificationVariables> are optional, but if present; their combination must identify a stratum.
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' Unlike \code{\link[RstoxFDA]{PSUSamplingParametersData}}, \code{\link[RstoxFDA]{AnalyticalPSUEstimateData}}, and, \code{\link[RstoxFDA]{IndividualSamplingParametersData}}, a stratum for 'AnalyticalPopulationEstimateData'
#' can be defined by several rows in the 'StratificationVariables' table.
#'
#' DomainVariables
#' \describe{
#' \item{Domain}{A domain, as identified in other tables.}
#' \item{DomainVariables}{Columns that relate the domain to data records.}
#' }
#'
#' SampleCount
#' \describe{
#' \item{Stratum}{A stratum, as identified in other tables.}
#' \item{Domain}{A domain, as identified in other tables.}
#' \item{nPSU}{The number of Primary Sampling Units observed for this domain in this stratum}
#' \item{nIndividuals}{The number of individuals observed for this domain in this stratum}
#' }
#'
#' @name AnalyticalPopulationEstimateData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if input is correctly formatted Analytical Population Estimate Data
#' @param table \code{\link[RstoxFDA]{AnalyticalPopulationEstimateData}}
#' @return validity
#' @concept Data types
#' @noRd
is.AnalyticalPopulationEstimateData <- function(AnalyticalPopulationEstimateData){
if (!is.list(AnalyticalPopulationEstimateData)){
return(FALSE)
}
if (!all(c("Abundance", "Variables", "AbundanceCovariance", "VariablesCovariance", "DomainVariables", "StratificationVariables", "SampleCount") %in% names(AnalyticalPopulationEstimateData))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "Abundance", "Frequency") %in% names(AnalyticalPopulationEstimateData$Abundance))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "Variable", "Total", "Mean") %in% names(AnalyticalPopulationEstimateData$Variables))){
return(FALSE)
}
if (!all(c("Stratum", "Domain1", "Domain2", "Variable1", "Variable2", "TotalCovariance", "MeanCovariance") %in% names(AnalyticalPopulationEstimateData$VariablesCovariance))){
return(FALSE)
}
if (!all(c("Stratum", "Domain1", "Domain2", "AbundanceCovariance", "FrequencyCovariance") %in% names(AnalyticalPopulationEstimateData$AbundanceCovariance))){
return(FALSE)
}
if (!all(c("Domain") %in% names(AnalyticalPopulationEstimateData$DomainVariables))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(AnalyticalPopulationEstimateData$StratificationVariables))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "nPSU", "nIndividuals") %in% names(AnalyticalPopulationEstimateData$SampleCount))){
return(FALSE)
}
domains <- nrow(AnalyticalPopulationEstimateData$DomainVariables) * length(unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum))
if (domains != nrow(AnalyticalPopulationEstimateData$Abundance)){
return(FALSE)
}
if (domains*length(unique(AnalyticalPopulationEstimateData$Variables$Variable)) != nrow(AnalyticalPopulationEstimateData$Variables)){
return(FALSE)
}
if (any(names(AnalyticalPopulationEstimateData$StratificationVariables != "Stratum"))){
stratavars <- names(AnalyticalPopulationEstimateData$StratificationVariables)[names(AnalyticalPopulationEstimateData$StratificationVariables)!="Stratum"]
stratvarString <- apply(AnalyticalPopulationEstimateData$StratificationVariables[,.SD,.SDcols = stratavars],
1, paste, collapse="/")
if (any(duplicated(stratvarString))){
return(FALSE)
}
if (any(is.na(AnalyticalPopulationEstimateData$StratificationVariables[,.SD,.SDcols = stratavars]))){
return(FALSE)
}
}
#check that strata is reported completely and consistently
for (nn in names(AnalyticalPopulationEstimateData)){
if ("Stratum" %in% names(AnalyticalPopulationEstimateData[[nn]])){
if (!all(AnalyticalPopulationEstimateData[[nn]]$Stratum
%in% AnalyticalPopulationEstimateData$StratificationVariables$Stratum)){
return(FALSE)
}
if (!all(AnalyticalPopulationEstimateData$StratificationVariables$Stratum %in%
AnalyticalPopulationEstimateData[[nn]]$Stratum)){
return(FALSE)
}
}
}
#check that domains are reported consistently
for (nn in names(AnalyticalPopulationEstimateData)){
if ("Domain" %in% names(AnalyticalPopulationEstimateData[[nn]])){
if (!all(AnalyticalPopulationEstimateData[[nn]]$Domain
%in% AnalyticalPopulationEstimateData$DomainVariables$Domain)){
return(FALSE)
}
}
}
return(TRUE)
}
#' PSU Sampling Design Parameters
#'
#' Sampling parameters for selection of Primary Sampling Units
#'
#' @details
#' Encodes information about the selection of Primary Sampling Units in multi-stage sampling, used in analytical design based estimation.
#' Information is encoded in three tables.
#'
#' The SampleTable encodes information about the sample of sampling units:
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum the sample is taken from. Treat unstratified sample as single-stratum sampling (provide only one stratum.}
#' \item{N}{Optional, num: The total number of PSUs in Stratum (total available for selection, not total selected)}
#' \item{n}{Optional, num: The number of PSUs selected from the Stratum}
#' \item{SelectionMethod}{Mandatory, chr: 'Poission', 'FSWR' or 'FSWOR'. The manner of selection for use in bootstrap or inference of inclusionProbabilities, selectionProbabilites, co-inclusion probabilities or co-selection probabilities.}
#' \item{FrameDescription}{Optional, chr: Free text field describing the sampling frame.}
#' }
#'
#' The SelectionTable encodes information abut the selection of sampling units for sampling:
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum the PSU is taken from.}
#' \item{Order}{Optional, num: Identifes the order of seleciton. May be necessary for inference when selections are not independent (e.g. FSWOR)}
#' \item{SamplingUnitId}{Optional, chr: Identifes PSU. NA encodes non-response}
#' \item{InclusionProbability}{Optional, num: The inclusion probability of the PSU}
#' \item{HTsamplingWeight}{Optional, num: The normalized Horvitz-Thompson sampling weight of the PSU}
#' \item{SelectionProbability}{Optional, num: The selection probability of the PSU}
#' \item{HHsamplingWeight}{Optional, num: The normalized Hansen-Hurwitz sampling weight of the PSU}
#' \item{SelectionDescription}{Optional, chr: Free text description of the PSU.}
#' }
#'
#' The StratificationVariables table encodes information about which columns in the sampleTable are stratification variables (if any):
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum. In addition the Stratum is identified by the combination of all other columns on this table.}
#' \item{<StratificationVariables>}{Mandatory if present (may not contain NAs), chr: Additional columns in the sampleTable that are stratification variables.}
#' }
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' The column 'Stratum' uniquely identifies a row in the table 'StratificationVariables'
#'
#' Optional columns may be NA.
#'
#' The selection methods available for 'SelectionMethod' are explained here:
#' \describe{
#' \item{Poission}{Poission sampling. Selection is performed randomly without replacement, and each selection is performed individually. Sample size is not fixed, and 'n' represents the expected sample size.}
#' \item{FSWR}{Fixed sample size with replacement. A random selection of a fixed sample size 'n' is chosen with replacement}
#' \item{FSWOR}{Fixed sample size without replacement. A random selection of a fixed sample size 'n' is chosen without replacement. Order of selection could be specified in the 'selectionTable'}
#' }
#'
#' \describe{
#' \item{The SelectionProbability is defined as:}{The probability of selecting the sampling unit when it was selected from the population.}
#' \item{The HHsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sampled unit when estimating with the Hansen-Hurwitz strategy: 1 / (SelectionProbability*Q) , where Q is the sum of the reciprocal of the SelectionProbabilites for the sampled units. For equal probability sampling with replacement, this is simply 1/n, where n i sample size.}
#' \item{The InclusionProbability is defined as:}{The probability of the sampling unit being included in the sample.}
#' \item{The HTsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sample when estimating with the Horvitz-Thompson strategy: 1 / (InclusionProbability*P), where P is the sum of the reciprocal of the InclusionProbabilites for the sampled units. For equal probability sampling without replacement, this is simply 1/n, where n is sample size.}
#' }
#'
#' @name PSUSamplingParametersData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if table is correctly formatted PSU Sampling Parameters Data
#' @param table \code{\link[RstoxFDA]{PSUSamplingParametersData}}
#' @return validity
#' @concept Data types
#' @noRd
is.PSUSamplingParametersData <- function(PSUSamplingParametersData){
if (!is.list(PSUSamplingParametersData)){
return(FALSE)
}
if (!all(sapply(PSUSamplingParametersData, data.table::is.data.table))){
return(FALSE)
}
if (!all(c("SampleTable", "SelectionTable", "StratificationVariables") %in% names(PSUSamplingParametersData))){
return(FALSE)
}
if (!all(c("Stratum", "N", "n", "SelectionMethod", "FrameDescription") %in% names(PSUSamplingParametersData$SampleTable))){
return(FALSE)
}
if (!all(c("Stratum", "Order", "SamplingUnitId", "InclusionProbability", "SelectionProbability", "HHsamplingWeight", "SelectionDescription") %in% names(PSUSamplingParametersData$SelectionTable))){
return(FALSE)
}
if (any(duplicated(PSUSamplingParametersData$SelectionTable$SamplingUnitId[!is.na(PSUSamplingParametersData$SelectionTable$SamplingUnitId)]))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(PSUSamplingParametersData$StratificationVariables))){
return(FALSE)
}
if (any(duplicated(PSUSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
#test that mandatory fields are not NA.
if (any(is.na(PSUSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$SampleTable$SelectionMethod))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$SelectionTable$Stratum))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
for (n in names(PSUSamplingParametersData$StratificationVariables)){
if (any(is.na(PSUSamplingParametersData$StratificationVariables[[n]]))){
return(FALSE)
}
}
if (ncol(PSUSamplingParametersData$StratificationVariables) > 1){
stratificationVariableStrings <- apply(PSUSamplingParametersData$StratificationVariables[,.SD, .SDcol=names(PSUSamplingParametersData$StratificationVariables)[names(PSUSamplingParametersData$StratificationVariables)!="Stratum"]], 1, paste, collapse="/")
duplicatedStrata <- PSUSamplingParametersData$StratificationVariables$Stratum[duplicated(stratificationVariableStrings)]
if (length(duplicatedStrata)>0){
return(FALSE)
}
}
if (any(duplicated(PSUSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
return(TRUE)
}
#' Individual Sub-Sampling Design Parameters
#'
#' Sampling parameters for selection of a sub-sample of individuals
#'
#' @details
#' Encodes information about the selection of a sub-sample of observations from individuals, used in analytical design based estimation.
#' A sub-sample is simply a sample of a sample. This data type is intended to represent the final stage of sampling in multi-stage sampling,
#' and therefore has a reference to the Sample it was taken from ('SampleId'). Apart from that there is no principal difference from single
#' stage sampling. All stratification is specified within the sample identifed by 'SampleId', and all sampling probabilites are specified within strata.
#'
#' The SampleTable encodes information about the sample of sampling units:
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the sub-sample is taken from.}
#' \item{Stratum}{Mandatory, chr: Identifies the within-sample stratum the sub-sample is taken from. Treat unstratified sample as single-stratum sampling (provide only one stratum. All strata with strata size > 0 must be reported for each SampleId.}
#' \item{N}{Optional, num: The total number of individuals in Stratum. For unstratified sampling, the total number of individuals in the sample the sub-sample is taken from.}
#' \item{n}{Optional, num: The number of individuals selected from the Stratum}
#' \item{SelectionMethod}{Mandatory, chr: 'Poission', 'FSWR' or 'FSWOR'. The manner of selection for use in bootstrap or inference of inclusionProbabilities, selectionProbabilites, co-inclusion probabilities or co-selection probabilities.}
#' \item{SampleDescription}{Optional, chr: Free text field describing the sample that is subsampled.}
#' }
#'
#' The SelectionTable encodes information abut the selection of sampling units for sampling:
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the sub-sample is taken from.}
#' \item{Stratum}{Mandatory, chr: Identifies the within sample-stratum the individual is taken from.}
#' \item{Order}{Optional, num: Identifes the order of seleciton. May be necessary for inference when selections are not independent (e.g. FSWOR)}
#' \item{IndividualId}{Optional, chr: Identifes individual. NA encodes non-response / observation failure}
#' \item{InclusionProbability}{Optional, num: The inclusion probability of the individual}
#' \item{HTsamplingWeight}{Optional, num: The normalized Horvitz-Thompson sampling weight of the individual}
#' \item{SelectionProbability}{Optional, num: The selection probability of the individual}
#' \item{HHsamplingWeight}{Optional, num: The normalized Hansen-Hurwitz sampling weight of the individual}
#' \item{SelectionDescription}{Optional, chr: Free text description of sampling unit.}
#' }
#'
#' The StratificationVariables table encodes information about which columns in the sampleTable are stratification variables (if any):
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the stratification applies to}
#' \item{Stratum}{Mandatory, chr: Identifies the within-sample stratum. In addition the Stratum is identified by the combination of all other columns on this table.}
#' \item{<StratificationVariables>}{Mandatory if present (may not contain NAs), chr: Additional columns in the sampleTable that are stratification variables.}
#' }
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' The combination of the columns 'Stratum' and 'SampleId' uniquely identifies a row in the table 'StratificationVariables'
#'
#' Optional columns may be NA.
#'
#' The selection methods available for 'SelectionMethod' are explained here:
#' \describe{
#' \item{Poission}{Poission sampling. Selection is performed randomly without replacement, and each selection is performed individually. Sample size is not fixed, and 'n' represents the expected sample size.}
#' \item{FSWR}{Fixed sample size with replacement. A random selection of a fixed sample size 'n' is chosen with replacement}
#' \item{FSWOR}{Fixed sample size without replacement. A random selection of a fixed sample size 'n' is chosen without replacement. Order of selection should be specified in the 'selectionTable'}
#' }
#'
#' \describe{
#' \item{The SelectionProbability is defined as:}{The probability of selecting the sampling unit when it was selected from the population.}
#' \item{The HHsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sampled unit when estimating with the Hansen-Hurwitz strategy: 1 / (SelectionProbability*Q) , where Q is the sum of the reciprocal of the SelectionProbabilites for the sampled units. For equal probability sampling with replacement, this is simply 1/n, where n i sample size.}
#' \item{The InclusionProbability is defined as:}{The probability of the sampling unit being included in the sample.}
#' \item{The HTsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sample when estimating with the Horvitz-Thompson strategy: 1 / (InclusionProbability*P), where P is the sum of the reciprocal of the InclusionProbabilites for the sampled units. For equal probability sampling without replacement, this is simply 1/n, where n is sample size.}
#' }
#'
#' @name IndividualSamplingParametersData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if table is correctly formatted Individual Sampling Parameters Data
#' @param table \code{\link[RstoxFDA]{IndividualSamplingParametersData}}
#' @return validity
#' @concept Data types
#' @noRd
is.IndividualSamplingParametersData <- function(IndividualSamplingParametersData){
if (!is.list(IndividualSamplingParametersData)){
return(FALSE)
}
if (!all(sapply(IndividualSamplingParametersData, data.table::is.data.table))){
return(FALSE)
}
if (!all(c("SampleTable", "SelectionTable", "StratificationVariables") %in% names(IndividualSamplingParametersData))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "N", "n", "SelectionMethod", "SampleDescription") %in% names(IndividualSamplingParametersData$SampleTable))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Order", "IndividualId", "InclusionProbability", "HTsamplingWeight", "SelectionProbability", "HHsamplingWeight", "SelectionDescription") %in% names(IndividualSamplingParametersData$SelectionTable))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(IndividualSamplingParametersData$StratificationVariables))){
return(FALSE)
}
if (any(duplicated(paste(IndividualSamplingParametersData$SampleTable$Stratum, IndividualSamplingParametersData$SampleTable$SampleId)))){
return(FALSE)
}
#test that mandatory fields are not NA.
if (any(is.na(IndividualSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SampleTable$SampleId))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SampleTable$SelectionMethod))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SelectionTable$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SelectionTable$SampleId))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$StratificationVariables$SampleId))){
return(FALSE)
}
for (n in names(IndividualSamplingParametersData$StratificationVariables)){
if (any(is.na(IndividualSamplingParametersData$StratificationVariables[[n]]))){
return(FALSE)
}
}
if (ncol(IndividualSamplingParametersData$StratificationVariables) > 2){
stratificationVariableStrings <- apply(IndividualSamplingParametersData$StratificationVariables[,.SD, .SDcol=names(IndividualSamplingParametersData$StratificationVariables)[!(names(IndividualSamplingParametersData$StratificationVariables) %in% c("Stratum"))]], 1, paste, collapse="/")
duplicatedStrata <- IndividualSamplingParametersData$StratificationVariables$Stratum[duplicated(stratificationVariableStrings)]
if (length(duplicatedStrata)>0){
return(FALSE)
}
}
return(TRUE)
}
#' Trip Partition
#'
#' Partitioning of catch from a trip.
#'
#' @details
#' list with two members 'fractions' and 'groupDefinition'
#'
#' 'fractions' is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{tripid}{trip identifier}
#' \item{species}{species identifier}
#' \item{groupid}{identifies the group the fraction is provided for. Groups are further specified in 'groupDefinition'}
#' \item{fraction}{fraction of the total catch of species in the given group}
#' }
#'
#' 'groupDefinition' is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{groupid}{identifies the group the defintion is provided for.}
#' \item{...}{one or more custom columns definint the group}
#' }
#'
#' @name TripPartition
#' @concept Data types
#'
NULL
#' Metier table
#'
#' Table (\code{\link[data.table]{data.table}}) defining metiers.
#'
#' Fishing activity metiers are approximate decompositions of fleets commonly used in Europe.
#' This table defines an approximate definition of metiers by a custom gear code.
#'
#' Metiers are used in EU-regulations for EU-member states, and are therefore often required by ICES data formats
#' Metiers are formal strings encoding decomposition an idealized fleet or set of trips, where target species and gear are clearly and unambigiously identified.
#' The metier system recognize that these parameters are not always clearly and unamibigiously identified to the desired resolution, and allows for grouping and omission of some parameters, and for coding that information is missing.
#' This makes the metier system very flexible, but also provides poor standardization and ICES databases and data-calls may provide code-lists of allowed metiers, sometimes with different metiers being requested for different areas.
#' In effect metier annotations cannot be completely standardized, but must to be configurable through conversion tables like this. Often the annotation has to be approximate.
#'
#' The metier system is spesified by the EU - Data Collection Framework: \url{https://datacollection.jrc.ec.europa.eu/wordef/fishing-activity-metier},
#' briefly a metier is fully specified by a string: <gear>_<target species>_<gear mesh size>_<selectivity device>_<selectivity device mesh size>_<vessel length class>,
#' with coding systems and grouping conventions defined for each of the variables.
#' Common trunctations of these strings are also used. E.g.:
#' Metier level 6 (no truncation): <gear>_<target species>_<gear mesh size>_<selectivity device>_<selectivity device mesh size>
#' Metier level 5 : <gear>_<target species>
#' Metier level 4 : <gear>
#' The term "metier" is also used for some derived systems, for instance some data-calls requests that a code for intended usage of catch (industrial vs human consumption) be appended to the metiers where gear and target species is missing.
#'
#' For example, in intercatch, the code OTB_DEF_>=120_0_0_all identifies bottom trawl with otter boards (OTB) targeting demershal fish (DEF), with mesh size larger than or equal to 120 mm, no selectivity device (0_0) and all vessel size (all)
#'
#' @details
#' \describe{
#' \item{metier}{character() metier-string, e.g.: OTB_DEF_>=120_0_0_all}
#' \item{gearcode}{character() encoding gear}
#' \item{target}{character(), optional, target species}
#' \item{meshedGear}{logical(), optional, whether the gear is a meshed gear. Should be provided for all or none gears.}
#' \item{lowerMeshSize}{integer(), optional, the lower mesh size to include in this metier. Should be provided for all rows where meshedGear is True, and not for other rows.}
#' \item{upperMeshSize}{integer(), optional, the upper mesh size to include in this metier. Should be provided for all rows where meshedGear is True, and not for other rows.}
#' \item{selectivityDevice}{character(), optional, encoding selectivity device.}
#' \item{meshedSelectivityDevice}{logical(), optional, encoding selectivity device. Should be provided for all or none selectivity devices.}
#' \item{selDevLowerMeshSize}{integer(), optional, the lower mesh size of selectivity device to include in this metier. Should be provided for all rows where meshedSelectivityDevice is True, and not for other rows.}
#' \item{selDevUpperMeshSize}{integer(), optional, the upper mesh size of selectivity device to include in this metier. Should be provided for all rows where meshedSelectivityDevice is True, and not for other rows.}
#' }
#'
#' The metier-defining parameters are written in camelCase, parameters that may be used to distinguish applicability of different metierdefinitions are writter in UPPER case.
#'
#' @name MetierTable
#' @concept Data types
#'
NULL
#' Check if table is correctly formatted metier table
#' @param table \code{\link[RstoxFDA]{MetierTable}}
#' @param throwError if set errors are raised, if not, validity will be returned as T/F
#' @return validity
#' @concept Data types
#' @noRd
is.MetierTable <- function(table, throwError=F){
if (!data.table::is.data.table(table)){
if (throwError){
stop("A metiertable must be a data.table")
}
return(FALSE)
}
if (!all(c("metier", "gearcode", "target", "meshedGear", "lowerMeshSize", "upperMeshSize", "selectivityDevice", "meshedSelectivityDevice", "selDevLowerMeshSize", "selDevUpperMeshSize") %in% names(table))){
if (throwError){
stop("Metiertable does not have the required columns")
}
return(FALSE)
}
if (!is.character(table$metier)){
if (throwError){
stop("The column 'metier' must be a character")
}
return(FALSE)
}
if (!is.character(table$gearcode)){
if (throwError){
stop("The column 'gearcode' must be a character")
}
return(FALSE)
}
if (!is.logical(table$meshedGear)){
if (throwError){
stop("The column 'meshedGear' must be a logical")
}
return(FALSE)
}
if (!is.numeric(table$lowerMeshSize)){
if (throwError){
stop("The column 'lowerMeshSize' must be an integer")
}
}
if (!is.numeric(table$upperMeshSize)){
if (throwError){
stop("The column 'upperMeshSize' must be an integer")
}
return(FALSE)
}
if (!is.character(table$selectivityDevice)){
if (throwError){
stop("The column 'selectivityDevice' must be a character")
}
return(FALSE)
}
if (!is.logical(table$meshedSelectivityDevice)){
if (throwError){
stop("The column 'meshedSelectivityDevice' must be a logical")
}
return(FALSE)
}
if (!is.numeric(table$selDevLowerMeshSize)){
if (throwError){
stop("The column 'selDevLowerMeshSize' must be an integer")
}
}
if (!is.numeric(table$selDevUpperMeshSize)){
if (throwError){
stop("The column 'selDevUpperMeshSize' must be an integer")
}
return(FALSE)
}
meshed <- table$meshedGear[!is.na(table$gearcode)]
if (any(is.na(meshed)) & !all(is.na(meshed))){
if (throwError){
stop("The column 'meshedGear' has a value for some gears, but not all")
}
return(FALSE)
}
upperMesh <- table$upperMeshSize[!is.na(table$meshedGear) & table$meshedGear]
if (any(is.na(upperMesh))){
if (throwError){
stop("The column 'upperMeshSize' is not provided for all meshed gears (where meshedGear is True)")
}
return(FALSE)
}
lowerMesh <- table$lowerMeshSize[!is.na(table$meshedGear) & table$meshedGear]
if (any(is.na(lowerMesh))){
if (throwError){
stop("The column 'lowerMeshSize' is not provided for all meshed gears (where meshedGear is True)")
}
return(FALSE)
}
if (any((!is.na(table$lowerMeshSize) | !is.na(table$upperMeshSize)) & (is.na(table$meshedGear) | !table$meshedGear))){
if (throwError){
stop("Mesh sizes provided for gears that are not meshed (where meshedGear is missing or False)")
}
return(FALSE)
}
meshedSel <- table$meshedSelectivityDevice[!is.na(table$gearcode) & !is.na(table$selectivityDevice)]
if (any(is.na(meshedSel)) & !all(is.na(meshedSel))){
if (throwError){
stop("The column 'meshedSelectivityDevice' has a value for some gears, but not all")
}
return(FALSE)
}
if (any((!is.na(table$lowerMeshSize) | !is.na(table$upperMeshSize)) & is.na(table$gear))){
if (throwError){
stop("Mesh sizes provided for gear where 'gear' is not given")
}
return(FALSE)
}
upperMeshSD <- table$selDevUpperMeshSize[!is.na(table$meshedSelectivityDevice) & table$meshedSelectivityDevice]
if (any(is.na(upperMeshSD))){
if (throwError){
stop("The column 'selDevUpperMeshSize' is not provided for all meshed selectivty devices gears (where meshedSelectivityDevice is True)")
}
return(FALSE)
}
lowerMeshSD <- table$selDevLowerMeshSize[!is.na(table$meshedSelectivityDevice) & table$meshedSelectivityDevice]
if (any(is.na(lowerMeshSD))){
if (throwError){
stop("The column 'selDevUpperMeshSize' is not provided for all meshed selectivty devices gears (where meshedSelectivityDevice is True)")
}
return(FALSE)
}
if (any((!is.na(table$selDevLowerMeshSize) | !is.na(table$selDevUpperMeshSize)) & (is.na(table$meshedSelectivityDevice) | !table$meshedSelectivityDevice))){
if (throwError){
stop("Mesh sizes provided for selectivity devices that are not meshed (where meshedSelectivityDevice is missing or False)")
}
return(FALSE)
}
if (any((!is.na(table$selDevLowerMeshSize) | !is.na(table$selDevUpperMeshSize)) & is.na(table$selectivityDevice))){
if (throwError){
stop("Mesh sizes provided for selectivity devices where 'selectivityDevice' is not given")
}
return(FALSE)
}
return(TRUE)
}
#' Check if input conforms to StoxBioticData.
#' Should perhaps be moved to RstoxData together with is.StoxLandingData
#' @noRd
is.StoxBioticData <- function(StoxBioticData, raiseErrors=F){
if (!is.list(StoxBioticData)){
return(FALSE)
}
if (!all(sapply(StoxBioticData, FUN=data.table::is.data.table))){
return(FALSE)
}
if (!all(c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample", "Individual") %in% names(StoxBioticData))){
return(FALSE)
}
if (any(duplicated(StoxBioticData$Cruise$Cruise))){
if (raiseErrors){
stop("Duplicate Cruise keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Station$Station))){
if (raiseErrors){
stop("Duplicate Station keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Haul$Haul))){
if (raiseErrors){
stop("Duplicate Haul keys")
}
return(FALSE)
}
# species category only have component key
if (any(duplicated(paste(StoxBioticData$SpeciesCategory$CruiseKey, StoxBioticData$SpeciesCategory$StationKey, StoxBioticData$SpeciesCategory$HaulKey, StoxBioticData$SpeciesCategory$SpeciesCategory)))){
if (raiseErrors){
stop("Duplicate SpeciesCategory keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Sample$Sample))){
if (raiseErrors){
stop("Duplicate Sample keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Individual$Individual))){
if (raiseErrors){
stop("Duplicate Individual keys")
}
return(FALSE)
}
return(TRUE)
}
#' LOCODE table (LocodeTable)
#'
#' @description
#' Table of United Nations Codes for Trade and Transport Locations (UN/LOCODE)
#'
#' \describe{
#' \item{Country}{ISO 3166 alpha-2 Country Code for the country in which the location is placed.}
#' \item{Location}{3-character code identifying the location within country}
#' \item{NameWoDiacritics}{Name of location without diacritic signs.}
#' \item{Subdivision}{ISO 1-3 character alphabetic and/or numeric code for the administrative division of the country concerned (the latter part of the complete ISO 3166-2 code element (after the hyphen).)}
#' \item{Status}{code for status of the code. E.g. Codes starting with A are various official approvals.}
#' \item{Function}{string with codes classifying the function of the location. If this contains 1, the location has a seaport.}
#' \item{Coordinates}{latitude and longitude in degrees and minutes.}
#' \item{Remarks}{general remark about location or change in coding}
#' \item{latitude}{latitude in decimal degrees}
#' \item{longitude}{longitude in decimal degrees}
#' }
#'
#'
#' @name LocodeTable
#' @concept Data types
#'
NULL
#' Kommune polygons
#'
#' @description
#' \code{\link[sf]{sf}} data.frame with area names identified in the column 'StratumName'. See \code{\link[RstoxBase]{StratumPolygon}}.
#'
#' Polygons are defined in WGS84 coordinates (unprojected).
#'
#' The data contains the following columns with rows for each area:
#' \describe{
#' \item{id}{Offical code for the muncipality (kommunenummer)}
#' \item{name}{Official name for the muncipality}
#' \item{StratumName}{Equal to id}
#' }
#'
#' @name KommunePolygons
#' @concept Data types
#' @import RstoxBase
#'
NULL
#' Landings archive (LandingsArchiveData)
#'
#' @description
#' Landings (aggregated sales notes). Format used for landings archive delivered to IMR by FDIR.
#' Additional documentation (in Norwegian) can be found in the files:
#' docs/documentation_landingsdata_archive_norwegian.csv
#' docs/selected_code_lists_landingsdata_archive_norwegian.xlsx
#' docs/supplementary_documentation_landingsdata_archive_norwegian.csv
#'
#' Note that 'AAR' and 'AAR2' denotes the year of catch, which may differ from the year of landing. The convention has been adopted to use 13 for 'LEVMND' (month of landing), when catch has been landed in January the year after catch.
#' \describe{
#' \item{AAR}{integer. Two last digits of year of catch}
#' \item{AAR2}{integer. Year of catch (4 digits)}
#' \item{FARTLAND}{character. Code for vessel flag (NOR for Norwegian, UTL for non-Norwegian)}
#' \item{LEVAAR}{integer. Two last digits of year of landing)}
#' \item{LEVMND}{integer. Month of landing (1=January, 12=December, 13=January following year)). Values larger than 13 are likely errors.}
#' \item{KYST}{integer. Code for whether the catch was caught within the coastal region (12 nautical miles from the coast). Code 0 denotes oceanic catch, code 8 and 9 denotes coastal catch.)}
#' \item{HOMR}{character. Main area of catch. As identified by the column 'StratumName' in \code{\link[RstoxFDA]{mainareaFdir2017}}), except that leading zeroes are not used for the areas 0-9.}
#' \item{LOK}{character. Location of catch. As identified by the column 'Lokasjon' in \code{\link[RstoxFDA]{locationsFdir2017}}), together with Main area (the column HAVOMR), except neither use leading zeroes.}
#' \item{REDS}{character. Gear code as defined by the standard NS9400. See code lists.}
#' \item{LEVHERRD}{character. Common official code for the muncipality (kommune) the catch was landed. Not using leading zeroes}
#' \item{LEVHERRD2}{character. Common official code for the muncipality (kommune) the catch was landed. Using leading zeroes}
#' \item{LEVFYLKE}{character. Common official code for the muncipality (fylke) the catch was landed. Same as the two leading digits in LEVHERRD2. Using leading zeroes}
#' \item{FISK}{character. Code for the species landed}
#' \item{FISK_NAVN}{character. Norwegian name of the species landed.}
#' \item{BIPROD}{character. Code for the product landed (0 codes for main-product, 1-8 codes for bi-products.)}
#' \item{ANVEND}{character. Code for the usage of the landing (human consumption vs industiral usage). See code lists.)}
#' \item{UTBET}{numeric. The prize paid to fisher in Norwegian currency at the time of purchase. Consult supplementary documentation for details.}
#' \item{VEKT}{numeric. Live weight (Round weight) of landed catch in kg. Listed as zero for bi-products.}
#' }
#'
#'
#' @name LandingsArchiveData
#' @concept Data types
#'
NULL
#' Logbooks (LstLogbookData)
#'
#' @description
#' Logbooks read from the lst format delivered by Directorate of Fisheries (FDIR).
#'
#' This format is not matched with WMS records and contains less detail than the format read by
#' \code{\link[RstoxData]{readErsFile}}
#'
#' Each row represent one fishing operation, which is defined in the legislation current at the time of reporting.
#'
#' Additional documentation (in Norwegian) can be found in the file:
#' doc/documentation_logbookdata_lst.xls
#' \describe{
#' \item{FAAR}{character. Year of catch (4 digits)}
#' \item{REGM}{character License number of fishing vessel (registreringsmerke)}
#' \item{FM}{character. Month of catch. number with leading zeroes. 1=January, 12=December}
#' \item{FD}{character. Day of catch. number with leading zeroes}
#' \item{DBNR}{character. Logbook number.}
#' \item{TUR}{character. Trip number.}
#' \item{FM}{character. Month of departure (start of trip). number with leading zeroes. 1=January, 12=December}
#' \item{FD}{character. Day of departure (start of trip). number with leading zeroes.}
#' \item{AH}{character. Port of departure (start of trip). Code identifying port.}
#' \item{LM}{character. Month of landing of catch. number with leading zeroes. 1=January, 12=December}
#' \item{LD}{character. Day of landing of catch. number with leading zeroes.}
#' \item{LH}{character. Port where catch was landed. Code identifying port.}
#' \item{RE}{character. Gear. Main gear for fishing operation definitions that allow several.}
#' \item{MA}{character. Mesh size (mm) for meshed gear.}
#' \item{HA}{character. Number of hauls/sets for fishing operation definitions that allow several.}
#' \item{VAR}{numeric. Total fishing time (hours).}
#' \item{OMRA}{character. International area code (ICES, NAFO, etc.)}
#' \item{OKSO}{character. Economic zone. Three letter code.}
#' \item{HO}{character. Main area of catch. As identified by the column 'StratumName' in \code{\link[RstoxFDA]{mainareaFdir2017}} or \code{\link[RstoxFDA]{mainareaFdir2018}})}
#' \item{LO}{character. Location of catch. As identified by the column 'Lokasjon' in \code{\link[RstoxFDA]{locationsFdir2017}} or \code{\link[RstoxFDA]{locationsFdir2018}}, together with Main area (the column HAVOMR)}
#' \item{LENG}{numeric. vessel length (m).}
#' \item{BTON}{character. Gross tonnage of vessel.}
#' \item{TENH}{character. Tonnage units of vessel.}
#' \item{HEST}{character. Engine effect of vessel (Hp)}
#' \item{FISK}{character. Code for the species landed. NS9400.}
#' \item{VEKT}{numeric Liveweight (Roundweight) in kg.}
#' }
#'
#'
#' @name LstLogbookData
#' @concept Data types
#'
NULL
#' Age group statistics (ReportFdaData)
#'
#' @description
#' Results from catch at age estimations. The results may be presented
#' decomposed on combinations of aggregation variables, such as gear, area, stock etc.
#'
#' ReportFdaData is a \code{\link[data.table]{data.table}} which may have the following columns:
#' \describe{
#' \item{AgeGroup}{character. The age group the estimate is reported for. May be age or plus group}
#' \item{Age}{integer. The lower age the estimate is reported for. May be an age or lower limit of plus group (inclusive)}
#' \item{LengthGroup}{character. The length group the estimate is reported for.}
#' \item{Length}{numeric. The upper length of the length group.}
#' \item{<Statistic>}{A reported statistic}
#' \item{SD}{Standard deviation for the reported statistic.}
#' \item{Low}{The lower limit of the estimated interval for the reported statistic.}
#' \item{High}{The higher limit of the estimated interval for the reported statistic.}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' @name ReportFdaData
#' @concept Data types
#'
NULL
#' Total catch statistics (ReportFdaSummaryData)
#'
#' @description
#' Results from catch estimations. The results may be presented
#' decomposed on combinations of aggregation variables, such as gear, area, stock etc.
#'
#' list with six members 'MeanAge', 'MeanWeight', 'MeanLength', 'TotalWeight',
#' 'TotalNumber', and 'GroupingVariables'.
#' 'MeanAge', 'MeanWeight', 'MeanLength', 'TotalWeight',
#' 'TotalNumber' are \code{\link[data.table]{data.table}}s with the columns:
#' \describe{
#' \item{<Statistic>}{The reported statistic, either 'MeanIndividualAge', 'MeanIndividualWeight', 'MeanIndividualLength', 'TotalWeight', or 'TotalNumber'}
#' \item{SD}{Standard deviation for the reported statistic.}
#' \item{Low}{The lower limit of the estimated interval for the reported statistic.}
#' \item{High}{The higher limit of the estimated interval for the reported statistic.}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaSummaryData
#' @concept Data types
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtAgeData)
#'
#' @description
#' A list with two members: 'NbyAge' and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CatchAtAge': the total catch at age in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaCatchAtAgeData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Covariance Report (ReportFdaCatchAtAgeCovarianceData)
#'
#' @description
#' Covariances from catch at age estimations. Covariances are presented
#' for age groups, together with any additional grouping (aggregation variables),
#' such as gear, area, stock etc.
#'
#' list with two members 'CovarianceNbyAge' and 'Variables'.
#' 'CovarianceNbyAge' is a \code{\link[data.table]{data.table}} which may have the following columns:
#' \describe{
#' \item{VariableId1}{Identifies the one of the variables the covariance is calculated for}
#' \item{VariableId2}{Identifies the other one of the variables the covariance is calculated for}
#' \item{Covariance}{The covariance of catch at age in numbers between groups identified by 'Variable1' and 'Variable2'.}
#' }
#' \describe{
#' \item{VariableId}{Identifier for variable that covariances are provided for.}
#' \item{AgeGroup}{character. The age group for the variable. May be age or plus group}
#' \item{Age}{integer. The lower age group for the variable. May be an age or lower limit of plus group (inclusive)}
#' \item{...}{Any aggregation variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#'
#' @name ReportFdaCatchAtAgeCovarianceData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtLengthData)
#'
#' @description
#' A list with two members: 'NbyLength', and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyLength}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CacthAtLength': the total catch at length in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#'
#' @name ReportFdaCatchAtLengthData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtLengthAndAgeData)
#'
#' @description
#' A list with two members: 'NbyLengthAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyLengthAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CatchAtAgeLength': the total catch at length and age in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaCatchAtLengthAndAgeData
#' @concept Data types
#'
NULL
#' Fisheries dependent Length At Age Report (ReportFdaLengthAtAgeData)
#'
#' @description
#' A list with two members: 'MeanLengthByAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{MeanLengthByAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'MeanIndividualLength': the mean length.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' Note that the summary statistics are reported for summaries of mean lengths,
#' so that e.g. SD report the standard deviation of the means,
#' and does not characterize the length distribution of fish.
#'
#' @name ReportFdaLengthAtAgeData
#' @concept Data types
#'
NULL
#' Reca Weight At Age Report (ReportFdaWeightAtAgeData)
#'
#' @description
#' A list with two members: 'MeanWeightByAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{MeanWeightByAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'MeanIndividualWeight': the mean weight}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' Note that the summary statistics are reported for summaries
#' of mean weights, so that e.g. SD report the standard deviation of the means,
#' and does not characterize the weight distribution of fish.
#'
#' @name ReportFdaWeightAtAgeData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ReportFdaData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaData}}
#' @param ReportFdaData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaData}}
#' @concept Data types
#' @export
is.ReportFdaData <- function(ReportFdaData){
if (!is.list(ReportFdaData)){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaData))){
return(FALSE)
}
if (length(ReportFdaData)<2){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaData[[1]])){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaData$GroupingVariables)){
return(FALSE)
}
if (!all(c("Age", "Low", "High", "SD") %in% names(ReportFdaData[[1]]))){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaData$GroupingVariables))){
return(FALSE)
}
return(TRUE)
}
#' Sum of Products report (ReportFdaSopData)
#'
#' @description
#' Sum of Products report (SOP-report), comparing the total landed weight of fish
#' with the product of mean weight at age estimates and total number
#' at age estimates
#'
#' list with two members 'SopReport' and 'GroupingVariables'.
#' 'SopReport' is a \code{\link[data.table]{data.table}} with the columns:
#' \describe{
#' \item{TotalWeightEstimated}{Total round weight estimated in kg}
#' \item{LandedWeight}{Landed round weight reported in kg}
#' \item{Difference}{The difference between estimated and reported landed weight in kg}
#' \item{RelativeDifference}{The difference between estimated and reported landed weight relative to reported weight}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' The unit for RelativeDifference is configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' @name ReportFdaSopData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @param ReportFdaSOP argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @concept Data types
#' @export
is.ReportFdaSOP <- function(ReportFdaSOP){
if (!is.list(ReportFdaSOP)){
return(FALSE)
}
if (!all(c("GroupingVariables", "SopReport") %in% names(ReportFdaSOP))){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSOP$SopReport)){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSOP$GroupingVariables)){
return(FALSE)
}
if (!all(c("TotalWeightEstimated", "LandedWeight", "Difference", "RelativeDifference") %in% names(ReportFdaSOP$SopReport))){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaSOP$GroupingVariables))){
return(FALSE)
}
return(TRUE)
}
#' Summary statistics for simulated parameters (ParameterizationSummaryData)
#'
#' @description
#' Summary statistics for (potentially multi-chained) simulated parameters,
#' such as MCMC simulations with Reca.
#' 'chains' in this respect refers to statistically independent simulations.
#'
#' list with two members 'ParameterSummary' and 'RunParameters',
#' which both all \code{\link[data.table]{data.table}}s. 'ParameterSummary'
#' contains parameter statistics for simulations and have the following columns:
#' \describe{
#' \item{Parameter}{Identifies the parameter that is summarized.}
#' \item{Mean}{Mean of the parameter}
#' \item{Variance}{Variance of the parameter}
#' \item{chainId}{Identifies the parameterization chain.}
#' }
#'
#' RunParameters summarizes global control parameters for each chain and has columns:
#' \describe{
#' \item{chainId}{Identifies the parameterization chain.}
#' \item{Iterations}{The number of iterations for parameter simulation}
#' }
#'
#' @seealso \code{\link[RstoxFDA]{RunRecaModels}} for running Reca-analysis
#' @seealso \code{\link[RstoxFDA]{ReportRecaParameterStatistics}}
#' for creating ParameterizationSummaryData from Reca-simulations.
#'
#' @name ParameterizationSummaryData
#' @concept Data types
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @param ParameterizationSummaryData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @concept Data types
#' @export
is.ParameterizationSummaryData <- function(ParameterizationSummaryData){
if (!is.list(ParameterizationSummaryData)){
return(FALSE)
}
if (!all(c("ParameterSummary", "RunParameters") %in% names(ParameterizationSummaryData))){
return(FALSE)
}
if (!data.table::is.data.table(ParameterizationSummaryData$ParameterSummary)){
return(FALSE)
}
if (!data.table::is.data.table(ParameterizationSummaryData$RunParameters)){
return(FALSE)
}
if (!all(c("chainId","Iterations") %in% names(ParameterizationSummaryData$RunParameters))){
return(FALSE)
}
if (!all(c("Parameter", "Mean", "Variance", "chainId") %in% names(ParameterizationSummaryData$ParameterSummary))){
return(FALSE)
}
return(TRUE)
}
#' Convergence Report for simulated parameters (ParameterConvergenceData)
#'
#' @description
#' Convergence Report for multi-chained simulated parameters,
#' such as MCMC simulations with Reca.
#' 'chains' in this respect refers to statistically independent simulations.
#'
#' list with one members 'ConvergenceReport',
#' which is a \code{\link[data.table]{data.table}} containing the following columns:
#' \describe{
#' \item{Parameter}{Identifies the parameter that is summarized.}
#' \item{InterVariance}{The Mean-Squared-Deviation the means of the parameter in each chain, to the mean across all chains}
#' \item{IntraVariance}{The mean of the within-chain variances of the parameter}
#' \item{GelmanRubinR}{Gelman-Rubins R}
#' }
#'
#' @details
#' Gelman-Rubins R is described by Gelman and Rubin (Statistical Science, 1992):
#' DOI: https://doi.org/10.1214/ss/1177011136
#'
#' @seealso \code{\link[RstoxFDA]{RunRecaModels}} for running Reca-analysis
#' @seealso \code{\link[RstoxFDA]{ReportParameterConvergence}}
#' for creating ParameterConvergenceData.
#'
#' @name ParameterConvergenceData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @param ParameterConvergenceData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @concept Data types
#' @export
is.ParameterConvergenceData <- function(ParameterConvergenceData){
if (!is.list(ParameterConvergenceData)){
return(FALSE)
}
if (!all(c("ConvergenceReport") %in% names(ParameterConvergenceData))){
return(FALSE)
}
cnames <- c("Parameter", "InterVariance", "IntraVariance", "GelmanRubinR")
if (!all(cnames %in% names(ParameterConvergenceData$ConvergenceReport))){
return(FALSE)
}
if (!data.table::is.data.table(ParameterConvergenceData$ConvergenceReport)){
return(FALSE)
}
return(TRUE)
}
#' Checks if argument is \code{\link[RstoxData]{Translation}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxData]{Translation}}
#' @param Translation argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxData]{Translation}}
#' @concept Data types
#' @export
is.Translation <- function(Translation){
if (!data.table::is.data.table(Translation)){
return(FALSE)
}
if (!all(c("NewValue") %in% names(Translation))){
return(FALSE)
}
if (ncol(Translation)<2){
return(FALSE)
}
return(TRUE)
}
#' Length Conversion Table (LengthConversionTable)
#'
#' @description
#' Length conversion parameters realting different length measurements.
#' @details
#' Length conversion factors relating different length measurements, such as 'standard length' and 'fork length'
#' based on a linear regression fit between length measurements:
#' L1 = alpha + beta \* L2,
#' where L1 and L2 are different length measurements
#' and 'alpha' and 'beta' are species-specific coefficients.
#'
#' \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{'Description'}{character: Free-text description of the product}
#' \item{'Species'}{character: Identifier for the species that the conversion applies to}
#' \item{'MeasurmentType'}{character: Identifier for the type of length measurement for the independent variable (L2 above)}
#' \item{'Alpha'}{numeric: scalar value representing the intercept (in cm) of a linear regression fit between length measurements.}
#' \item{'Beta'}{numeric: scalar value representing the slope of a linear regression fit between length measurements.}
#' }
#'
#' @name LengthConversionTable
#' @concept Data types
#'
NULL
#' Check if argument is LengthConversionTable
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{LengthConversionTable}}
#' @param LengthConversionTable argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{LengthConversionTable}}
#' @concept Data types
#' @export
is.LengthConversionTable <- function(LengthConversionTable){
if (!data.table::is.data.table(LengthConversionTable)){
return(FALSE)
}
if (!all(c("Description", "Species", "MeasurementType", "Alpha", "Beta") %in% names(LengthConversionTable))){
return(FALSE)
}
return(TRUE)
}
#' Weight Conversion Table (WeightConversionTable)
#'
#' @description
#' \code{\link[data.table]{data.table}} with factors for approximating the weight of a
#' desired product type (e.g. round fish)
#' from weights of other fish products. Contains the columns:
#' \describe{
#' \item{'Description'}{Free-text description of the product type}
#' \item{'Species'}{Identifier for the species that the conversion applies to}
#' \item{'ProductType'}{Identifier for the type of product that the conversion applies to}
#' \item{'WeightFactor'}{scalar value that weights for the given 'ProductType' can be multiplied with to approximate desired product type (e.g. round fish).}
#' }
#' NA is allowed for 'WeightFactor', which will result in NA for weights after conversion
#'
#' @name WeightConversionTable
#' @concept Data types
#'
NULL
#' Check if argument is WeightConversionTable
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{WeightConversionTable}}
#' @param WeightConversionTable argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{WeightConversionTable}}
#' @concept Data types
#' @export
is.WeightConversionTable <- function(WeightConversionTable){
if (!data.table::is.data.table(WeightConversionTable)){
return(FALSE)
}
if (!all(c("Description", "Species", "ProductType", "WeightFactor") %in% names(WeightConversionTable))){
return(FALSE)
}
return(TRUE)
}
#' Spatial landings overview data (PlotFisheriesOverviewSpatialData)
#'
#' @description
#' a ggplot object that renders catch density of landings on polygons.
#'
#' @name PlotFisheriesOverviewSpatialData
#' @concept Data types
#'
NULL
#' Temporal landings overview data (PlotFisheriesOverviewTemporalData)
#'
#' @description
#' a ggplot object that renders landings by date of catch and by grouping variables.
#'
#' @name PlotFisheriesOverviewTemporalData
#' @concept Data types
#'
NULL
#' Sampling Overview cell-plot data (PlotSamplingOverviewCellData)
#'
#' @description
#' a ggplot object that renders the a colored 'cell plot' overview of samples and landings.
#'
#' @name PlotSamplingOverviewCellData
#' @concept Data types
#'
NULL
#' Sampling variables plot data (PlotSamplingCoverageData)
#'
#' @description
#' a ggplot object that renders a a barplot of landings with color codes for how much each part of the fishery is sampled.
#'
#' @name PlotSamplingCoverageData
#' @concept Data types
#'
NULL
#' Sampling variables plot data (PlotSamplingVariablesData)
#'
#' @description
#' a ggplot object that renders a stacked barplot of sampling variables for each part of the fishery, with total landings on a secondary axis.
#'
#' @name PlotSamplingVariablesData
#' @concept Data types
#'
NULL
#' Trace plot (PlotPosteriorTracesData)
#'
#' @description
#' a ggplot object that renders traceplot of Reca predictions.
#'
#' @name PlotPosteriorTracesData
#' @concept Data types
#'
NULL
#' Total catch by age plot (PlotCatchAtAgeTotalsData)
#'
#' @description
#' a ggplot object that renders estimated total catch by age.
#'
#' @name PlotCatchAtAgeTotalsData
#' @concept Data types
#'
NULL
#' Mean weight at age plot (PlotMeanWeightAtAgeData)
#'
#' @description
#' a ggplot object that renders estimated mean weight by age.
#'
#' @name PlotMeanWeightAtAgeData
#' @concept Data types
#'
NULL
#' Mean length at age plot (PlotMeanLengthAtAgeData)
#'
#' @description
#' a ggplot object that renders estimated mean length by age.
#'
#' @name PlotMeanLengthAtAgeData
#' @concept Data types
#'
NULL
#' Sampling Report data (ReportFdaSamplingData)
#'
#' @description
#' list with tow members:
#' \describe{
#' \item{GroupingVariables}{a \code{\link[data.table]{data.table}} with the variables used for aggregation in 'FisheriesSampling' stored in the column 'GroupingVariables'}
#' \item{SamplingVariables}{a \code{\link[data.table]{data.table}} with the variables used for partitioning samples in 'FisheriesSampling' stored in the column 'SamplingVariables'}
#' \item{FisheriesSampling}{a \code{\link[data.table]{data.table}} described below.}
#' }
#'
#' FisheriesSampling is a report of sampling against total landings for partitions of a fishery.
#' The report is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{...}{A column for each of the provided Aggregation variables}
#' \item{...}{A column for each of the provided Sampling variables}
#' \item{LandedRoundWeight}{Total landings in kg}
#' \item{Catches}{Number of unique catches sampled}
#' \item{Vessels}{Number of unique vessels sampled}
#' \item{WeightMeasurements}{Number of fished measured for weight}
#' \item{LengthMeasurements}{Number of fished measured for length}
#' \item{AgeReadings}{Number of fished with age determined}
#' \item{WeightOfSampledCatches}{Total weight of the sampled catches}
#' }
#'
#' @name ReportFdaSamplingData
#' @concept Data types
#'
NULL
#' Check if argument is ReportFdaSamplingData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSamplingData}}
#' @param ReportFdaSamplingData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSamplingData}}
#' @concept Data types
#' @export
is.ReportFdaSamplingData <- function(ReportFdaSamplingData){
if (!is.list(ReportFdaSamplingData)){
return(FALSE)
}
if (!all(c("GroupingVariables", "SamplingVariables", "FisheriesSampling") %in% names(ReportFdaSamplingData))){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSamplingData$FisheriesSampling)){
return(FALSE)
}
if (!all(c("LandedRoundWeight", "Catches", "Vessels", "WeightMeasurements", "LengthMeasurements", "AgeReadings", "WeightOfSampledCatches") %in% names(ReportFdaSamplingData$FisheriesSampling))){
return(FALSE)
}
return(TRUE)
}
#' Landings Report data (ReportFdaLandingData)
#'
#' @description
#' list with tow members:
#' \describe{
#' \item{GroupingVariables}{a \code{\link[data.table]{data.table}} with the variables used for aggregation in 'FisheriesLandings' stored in the column 'GroupingVariables'}
#' \item{FisheriesLandings}{a \code{\link[data.table]{data.table}} described below.}
#' }
#'
#' FisheriesLandings is a report of landings for partitions of a fishery.
#' The report is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{...}{A column for each of the provided Aggregation variables}
#' \item{LandedRoundWeight}{Total landings in kg}
#' }
#'
#' @name ReportFdaLandingData
#' @concept Data types
#'
NULL
#' Reca Data (RecaData)
#'
#' Data and some data parameters prepared for running
#' \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}
#' via \code{\link[RstoxFDA]{RunRecaEstimate}}.
#'
#' A list with five members:
#'
#' @details
#' \describe{
#' \item{AgeLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{WeightLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{Landings}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{GlobalParameters}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. see details. List of \code{\link[data.table]{data.table}}}
#' \item{CovariateMaps}{Mapping of values for each covariate in landings and samples (including non-configurable catchId) to integer value used in R-ECA. List of \code{\link[data.table]{data.table}}}
#' }
#'
#' @name RecaData
#' @concept Data types
#'
NULL
#' Check if argument is RecaData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaData}}
#' @param RecaData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaData}}
#' @concept Data types
#' @export
is.RecaData <- function(RecaData){
if (!is.list(RecaData)){
return(FALSE)
}
if (!all(c("AgeLength", "WeightLength", "Landings", "GlobalParameters") %in% names(RecaData))){
return(FALSE)
}
if (!is.list(RecaData$AgeLength)){
return(FALSE)
}
if (!is.list(RecaData$WeightLength)){
return(FALSE)
}
if (!is.list(RecaData$Landings)){
return(FALSE)
}
if (!is.list(RecaData$GlobalParameters)){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaData$AgeLength))){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaData$WeightLength))){
return(FALSE)
}
if (!all(c("AgeLengthCov", "WeightLengthCov", "LiveWeightKG") %in% names(RecaData$Landings))){
return(FALSE)
}
return(TRUE)
}
#' Reca Parameter Data (RecaParameterData)
#'
#' @description
#' Data and some data parameters prepared for running
#' various report functions that invoke \code{\link[Reca]{eca.predict}}.
#'
#' @section model fit:
#' For inspection or analysis of model fit, the lists 'FitProportionAtAge',
#' 'FitLengthGivenAge' and 'FitWeightGivenLength' is of interest. For stock-splitting analysis,
#' the lists FitLengthGivenAgeCC and FitWeightGivenLengthCC will be added as well, corresponding to one of the stocks.
#' These lists correspond to the three Reca-models and contain:
#' \describe{
#' \item{LogLikeliehood}{A \code{\link[data.table]{data.table}}
#' tabulating the logarithm of the likeliehood of the parameter set for each iteration}
#' \item{...}{A \code{\link[data.table]{data.table}} for each of the model effects (e.g. covariates).}
#' }
#'
#' In addition to configurable covariates, the models always contain a constant effect (named 'constant'),
#' a catch or haul effect (named 'catchSample') and effects for fish measurements (named 'fish').
#' Where relevant the following parameters may be tabulated for each effect:
#' \describe{
#' \item{Age}{Identifying the age the effect applies to}
#' \item{Level}{Identifying the value or level of the covariate the effect applies to}
#' \item{Iteration}{Identifying the iteration the fit is provided for}
#' \item{AgeIndex}{Age identifier used internally in Reca}
#' \item{LevelIndex}{Level identifier used internally in Reca}
#' \item{Slope}{The value of the regression slope}
#' \item{tau_Slope}{The value of tau parameter for the regression slope}
#' \item{ar_Slope}{The value of regression slope of a the autoregressive coefficient associated with the effect}
#' \item{Intercept}{The value of the regression intercept}
#' \item{tau_Intercept}{The value of tau parameter for the regression intercept}
#' \item{ar_Intercept}{The value of the regression intercept of a autoregressive coefficient associated with the effect}
#' }
#' Consult Hirst et.al. 2005 for description of the parameters
#'
#' @section other data:
#' The lists 'AgeLength', 'WeightLength', 'Landings', 'GlobalParameters' and 'CovariateMaps'
#' may be passed to \code{\link[Reca]{eca.predict}} in functions consuming output from this function. All in all
#' the following lists can be accessed on RecaParameterData objects:
#' \describe{
#' \item{FitProportionAtAge}{list of data tables with parameters for for the Proportion-at-age model}
#' \item{FitLengthGivenAge}{list of data tables with parameters for for the Length-given-age model}
#' \item{FitWeightGivenLength}{list of data tables with parameters for for the Weight-given-length model}
#' \item{AgeLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{WeightLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{Landings}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{GlobalParameters}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. see details}
#' \item{CovariateMaps}{Mapping of values for each covariate in landings and samples (including non-configurable catchId) to integer value used in R-ECA.}
#' }
#'
#' @name RecaParameterData
#' @concept Data types
#'
NULL
#' Check if argument is RecaParameterData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaParameterData}}
#' @param RecaParameterData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaParameterData}}
#' @concept Data types
#' @export
is.RecaParameterData <- function(RecaParameterData){
if (!is.list(RecaParameterData)){
return(FALSE)
}
if (!all(c("FitProportionAtAge", "FitLengthGivenAge", "FitWeightGivenLength", "AgeLength", "WeightLength", "Landings", "GlobalParameters") %in% names(RecaParameterData))){
return(FALSE)
}
if (!is.list(RecaParameterData$AgeLength)){
return(FALSE)
}
if (!is.list(RecaParameterData$WeightLength)){
return(FALSE)
}
if (!is.list(RecaParameterData$Landings)){
return(FALSE)
}
if (!is.list(RecaParameterData$GlobalParameters)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitProportionAtAge)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitLengthGivenAge)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitWeightGivenLength)){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaParameterData$AgeLength))){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaParameterData$WeightLength))){
return(FALSE)
}
if (!all(c("AgeLengthCov", "WeightLengthCov", "LiveWeightKG") %in% names(RecaParameterData$Landings))){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitProportionAtAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitProportionAtAge$LogLikelihood)){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitLengthGivenAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitLengthGivenAge$LogLikelihood)){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitWeightGivenLength))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitWeightGivenLength$LogLikelihood)){
return(FALSE)
}
return(TRUE)
}
#' Reca Results (RecaCatchAtAge)
#'
#' @description
#' Posterior distribution of total catch at age and weight and length parameters.
#'
#' @details
#' a list of data tables:
#' \describe{
#' \item{CatchAtAge}{\code{\link[data.table]{data.table}} tabulating the estimated catch-at-age by length group for each Reca iteration (MCMC sample)}
#' \item{MeanLength}{\code{\link[data.table]{data.table}} tabulating the mean length in cm by age for each Reca iteration (MCMC sample)}
#' \item{MeanWeight}{\code{\link[data.table]{data.table}} tabulating the mean weight in g by age for each Reca iteration (MCMC sample)}
#' \item{GroupingVariables}{\code{\link[data.table]{data.table}} with any variables that catch-at-age estimates are partitioned on
#' in the column 'GroupingVariables'. These may correspond to variables in the landings, or maye be the variable 'Stock' if
#' stock-splitting analysis have been perfomred.}
#' }
#' In addition to columns for the variables in 'GroupingVariables', the data tables 'CatchAtAge', 'MeanLength', and 'MeanWeight' have the following variables:
#' \describe{
#' \item{Age}{Age in number of years.}
#' \item{Iteration}{The Reca iteration (MCMC sample) that estimates are calculated for}
#' }
#'
#' 'CatchAtAge' also have the variables
#' \describe{
#' \item{Length}{Upper limit of length group in cm}
#' \item{CatchAtAge}{The total catch at age in numbers}
#' }
#'
#' 'MeanLength' has the variable:
#' \describe{
#' \item{MeanIndividualLength}{Mean Length at age in cm}
#' }
#'
#' 'MeanWeight' has the variable:
#' \describe{
#' \item{MeanIndividualWeight}{Mean weight at age in g}
#' }
#'
#' @name RecaCatchAtAge
#' @concept Data types
#'
NULL
#' Check if argument is RecaCatchAtAge
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaCatchAtAge}}
#' @param RecaCatchAtAge argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaCatchAtAge}}
#' @concept Data types
#' @export
is.RecaCatchAtAge <- function(RecaCatchAtAge){
if (!is.list(RecaCatchAtAge)){
return(FALSE)
}
if (!all(c("CatchAtAge", "MeanLength", "MeanWeight") %in% names(RecaCatchAtAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$CatchAtAge)){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$MeanLength)){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$MeanWeight)){
return(FALSE)
}
if (!all(c("Length", "Age", "Iteration", "CatchAtAge") %in% names(RecaCatchAtAge$CatchAtAge))){
return(FALSE)
}
if (!all(c("MeanIndividualLength", "Age", "Iteration") %in% names(RecaCatchAtAge$MeanLength))){
return(FALSE)
}
if (!all(c("MeanIndividualWeight", "Age", "Iteration") %in% names(RecaCatchAtAge$MeanWeight))){
return(FALSE)
}
return(TRUE)
}
#' Reca Results (RecaResult)
#'
#' Results from running
#' \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}
#' via \code{\link[RstoxFDA]{RunRecaEstimate}}.
#'
#' @details
#'
#' \describe{
#' \item{input}{All input data and parameters provided to \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{fit}{as returned by \code{\link[Reca]{eca.estimate}}}
#' \item{prediction}{as returned by \code{\link[Reca]{eca.predict}}}
#' \item{covariateMaps}{list() mapping from Reca covariate encoding to values fed to \code{\link[RstoxFDA]{PrepareRecaEstimate}}. As in \code{\link[RstoxFDA]{RecaData}}}
#' }
#'
#' @name RecaResult
#' @concept Data types
#'
NULL
#' @noRd
is.RecaPrediction <- function(prediction){
if (!is.list(prediction)){
return(FALSE)
}
if (!all(c("TotalCount", "MeanLength", "MeanWeight", "AgeCategories", "LengthIntervalsLog") %in% names(prediction))){
return(FALSE)
}
if (!is.array(prediction$TotalCount)){
return(FALSE)
}
if (!is.array(prediction$MeanLength)){
return(FALSE)
}
if (!is.array(prediction$MeanWeight)){
return(FALSE)
}
return(TRUE)
}
#' Check if argument is RecaResult
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaResult}}
#' @param RecaResult argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaResult}}
#' @concept Data types
#' @export
is.RecaResult <- function(RecaResult){
if (!is.list(RecaResult)){
return(FALSE)
}
if (!all(c("input", "fit", "prediction", "covariateMaps") %in% names(RecaResult))){
return(FALSE)
}
if (!is.RecaData(RecaResult$input)){
return(FALSE)
}
if (!is.RecaPrediction(RecaResult$prediction)){
return(FALSE)
}
return(TRUE)
}
#' Temporal Categories (TemporalDefinition)
#'
#' Table (\code{\link[data.table]{data.table}}) defining a categorical variable for grouping data based on date.
#'
#' @details
#' \describe{
#' \item{TemporalCategory}{character() Value of the temporal category}
#' \item{StartDay}{integer() Day of month for first day in the temporal category (1-based)}
#' \item{StartMonth}{integer() Month for first day in the temporal category (1-based)}
#' \item{StartYear}{optional integer() Year for which the category is defined, omit this column for seasonal definitions.}
#' }
#'
#' Start and end of year is not implied as category delimitations when not included.
#' If 1st of January is not defined as the start of a category,
#' it is taken to be included in the last category of the preceding year.
#'
#' @name TemporalDefinition
#' @concept Data types
#'
NULL
#' Check if argument is TemporalDefinition
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{TemporalDefinition}}
#' @param TemporalDefinition argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{TemporalDefinition}}
#' @concept Data types
#' @export
is.TemporalDefinition <- function(TemporalDefinition){
if (!data.table::is.data.table(TemporalDefinition)){
return(FALSE)
}
#StartYear is optional.
if (!all(c("Period", "StartDay", "StartMonth") %in% names(TemporalDefinition))){
return(FALSE)
}
if (!all(names(TemporalDefinition) %in% c("Period", "StartDay", "StartMonth", "StartYear"))){
return(FALSE)
}
return(TRUE)
}
#' Area Code Positions (AreaPosition)
#'
#' Table (\code{\link[data.table]{data.table}}) defining a position for area codes.
#'
#' @details
#' \describe{
#' \item{Area}{Area code. (key)}
#' \item{Location}{optional subdivision of 'Area'}
#' \item{Latitude}{WGS84 Latitude, decimal degrees}
#' \item{Longitude}{WGS84 Longitude, decimal degrees}
#' }
#' If location is provided, the case for missing location is also encoded.
#'
#' @name AreaPosition
#' @concept Data types
#'
NULL
#' Check if argument is AreaPosition
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{AreaPosition}}
#' @param AreaPosition argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{AreaPosition}}
#' @concept Data types
#' @export
is.AreaPosition <- function(AreaPosition){
if (!data.table::is.data.table(AreaPosition)){
return(FALSE)
}
if (!all(c("Area", "Location", "Latitude", "Longitude") %in% names(AreaPosition))){
return(FALSE)
}
return(TRUE)
}
#' Area Neighbour Definition (CarNeighbours)
#'
#' Table (\code{\link[data.table]{data.table}})
#' defining neighbours for a CAR-variable (Conditional autoregressive variable).
#'
#' @details
#' \describe{
#' \item{CarValue}{Values for a variable used as CAR-variable}
#' \item{Neighbours}{Comma-separated list of neighbours}
#' }
#'
#' The table is symmetric, so that if b is a neighbour of a. a is also a neighbour of b.
#'
#' @name CarNeighbours
#' @concept Data types
#'
NULL
#' Landing data (LandingData)
#'
#' @description
#' See \code{\link[RstoxData]{LandingData}}
#'
#' @name LandingData
#' @concept Data types
#'
NULL
#' Check if argument is CarNeighbours
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{CarNeighbours}}
#' @param CarNeighbours argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{CarNeighbours}}
#' @concept Data types
#' @export
is.CarNeighbours <- function(CarNeighbours){
if (!data.table::is.data.table(CarNeighbours)){
return(FALSE)
}
if (!all(c("CarValue", "Neighbours") %in% names(CarNeighbours))){
return(FALSE)
}
return(TRUE)
}
#' Age Error Matrix (AgeErrorMatrix)
#'
#' Table (\code{\link[data.table]{data.table}})
#' defining probabilities of misreading age.
#'
#' @details
#' \describe{
#' \item{columns 1..n}{numeric() [0,1]. Probability of reading read age, given that true age is as column name.}
#' \item{ReadAge}{The read age.}
#' }
#'
#' Columns sum to 1.
#'
#' @name AgeErrorMatrix
#' @concept Data types
#'
NULL
#' Check if argument is AgeErrorMatrix
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{AgeErrorMatrix}}
#' @param AgeErrorMatrix argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{AgeErrorMatrix}}
#' @concept Data types
#' @export
is.AgeErrorMatrix <- function(AgeErrorMatrix){
if (!data.table::is.data.table(AgeErrorMatrix)){
return(FALSE)
}
if (!("ReadAge" %in% names(AgeErrorMatrix))){
return(FALSE)
}
return(TRUE)
}
#' Stock splitting parameters (StockSplittingParameters)
#'
#' @description
#' Table (\code{\link[data.table]{data.table}})
#'
#' Defining parameters for the stock-splitting analysis in Reca, including
#' parameters for the probability of misclassifying when determining stock membership of a specimen.
#'
#' The stock splitting analysis allows catch at age to be estimated for two domains that partition all individuals,
#' and that are observed for all age-determined specimens. It was developed for disciminating Coastal Cod and North East Arctic Cod,
#' based on otholith growth patterns, and naming conventions are derived from that. It could be adapted to
#' other stocks and in principle to other bipartite domain definitions (such as Sex).
#'
#' Two otolith patterns are defined for each of the two stocks 'CC' and 'S'. Otolith type 1 and 2 identifies
#' that a specimen belongs to the stock 'CC', and are interpreted by otoloith readers as 'certain' and 'uncertain' CC, respectively.
#' Otolith type 4 and 5 identifies that a specimen belongs to the stock 'S', and are interpreted as 'uncertain' and 'certain' S, respectively.
#'
#' \describe{
#' \item{StockNameCC}{Name of the stock identified as CC}
#' \item{StockNameS}{Name of the stock identified as S}
#' \item{ProbabilityType1As1}{Probability of classifying a type 1 specimen as type 1 (certain CC).}
#' \item{ProbabilityType5As1}{Probability of classifying a type 5 (certain S) specimen as type 1 (certain CC).}
#' \item{ProbabilityType2As2}{Probability of classifying a type 2 (uncertain CC) specimen as type 2 (uncertain CC).}
#' \item{ProbabilityType4As2}{Probability of classifying a type 4 (uncertain S) specimen as type 2 (uncertain CC).}
#' \item{ProbabilityType2As4}{Probability of classifying a type 2 (uncertain CC) specimen as type 4 (uncertain S).}
#' \item{ProbabilityType4As4}{Probability of classifying a type 4 (uncertain S) specimen as type 4 (uncertain S).}
#' \item{ProbabilityType1As5}{Probability of classifying a type 1 (certain CC) specimen as type 5 (certain S).}
#' \item{ProbabilityType5As5}{Probability of classifying a type 5 (certain S) specimen as type 5 (certain S).}
#' }
#'
#' The probabilities for different ways to classify a type must sum to 1.
#' E.g.: ProbabilityType1As1 + ProbabilityType1As5 = 1.
#'
#' The data table contains only one row.
#'
#' @name StockSplittingParameters
#' @concept Data types
#'
NULL
#' Check if argument is StockSplittingParameters
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{StockSplittingParameters}}
#' @param StockSplittingParameters argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{StockSplittingParameters}}
#' @concept Data types
#' @export
is.StockSplittingParameters <- function(StockSplittingParameters){
if (!data.table::is.data.table(StockSplittingParameters)){
return(FALSE)
}
if (!all(c("StockNameCC", "StockNameS", "ProbabilityType1As1",
"ProbabilityType1As5", "ProbabilityType2As2",
"ProbabilityType2As4", "ProbabilityType4As2",
"ProbabilityType4As4", "ProbabilityType5As1",
"ProbabilityType5As5") %in% names(StockSplittingParameters))){
return(FALSE)
}
if (nrow(StockSplittingParameters) != 1){
return(FALSE)
}
prob <- function(arg){
if (arg<0 | arg > 1){
return(FALSE)
}
return(TRUE)
}
if (!prob(StockSplittingParameters$ProbabilityType1As1)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType1As5)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType2As2)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType2As4)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType4As2)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType4As4)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType5As1)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType5As5)){
return(FALSE)
}
return(TRUE)
}
plotDefaultsCellPlotColors <- list(
MinVessels=2,
MinCatches=2,
MinMeasurements=100,
ColorNoSamples = "#ffffcc",
ColorFewMeasurements = "#c2e699",
ColorFewCatches = "#78c679",
ColorFewVessels = "#31a354",
ColorGoodSampling = "#006837"
)
#' Function specification for inclusion in StoX UI
#' @export
stoxFunctionAttributes <- list(
ReadLandingFDA = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "LandingData",
functionParameterFormat = list(FileNames = "filePaths"),
functionArgumentHierarchy = list()
),
DefineStockSplittingParameters = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "StockSplittingParameters",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
),
StockNameCC=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
StockNameS=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType1As1=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType5As1=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType2As2=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType4As2=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType2As4=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType4As4=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType1As5=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType5As5=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
)
)
),
DefineCarNeighbours = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "CarNeighbours",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
StratumPolygon = list(
DefinitionMethod = "StratumPolygon",
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefineAgeErrorMatrix = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "AgeErrorMatrix",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
)
)
),
DefineAreaPosition = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "AreaPosition",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
StratumPolygon = list(
DefinitionMethod = "StratumPolygon",
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefinePeriod = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "TemporalDefinition",
functionParameterFormat = list(
CustomPeriods = "periodvector"
),
functionArgumentHierarchy = list(
TemporalCategory = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
CustomPeriods = list(
TemporalCategory = "Custom",
UseProcessData = FALSE
)
)
),
DefineWeightConversionFactor = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "WeightConversionTable",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefineLengthConversionParameters = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "LengthConversionTable",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
LoadFdaStratumPolygon = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "StratumPolygon",
functionArgumentHierarchy = list(
StrataSystem = list(
UseProcessData = FALSE
)
)
),
ConvertWeightBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
ConvertLengthBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetTimeBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetShortGearBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetStartDateBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetAreaPositionsBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
AddAreaPositionStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData"
),
AddGearGroupStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData"
),
AddGearGroupStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
AddStratumStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData",
functionParameterDefaults = list(
ColumnName = "Stratum"
)
),
AddStratumStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
ColumnName = "Stratum"
)
),
AddPeriodStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData",
functionParameterDefaults = list(
ColumnName = "Period"
)
),
AddPeriodStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
ColumnName = "Period"
)
),
ComputeIndividualSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "IndividualSamplingParametersData",
functionParameterFormat = list(
Parameters = "individualparameters",
StratificationColumns = "individualstratificationcolumns"
),
functionArgumentHierarchy = list(
StratificationColumns = list(
DefinitionMethod = "Stratified"
),
LengthInterval = list(
DefinitionMethod = "LengthStratified"
)
)
),
CollapseStrata = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "IndividualSamplingParametersData",
functionParameterFormat = list(
RetainStrata = "collapsestrataretain"
)
),
ComputePSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
SamplingUnitId = "samplingunitid",
StratificationColumns = "stratificationcolumns"
),
functionParameterDefaults = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
functionArgumentHierarchy = list(
StratificationColumns = list(
DefinitionMethod = "AdHocStoxBiotic"
),
StratumName = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
Quota = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
ExpectedSampleSize = list(
DefinitionMethod = "ProportionalPoissonSampling"
)
)
),
ReadPSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
FileName = "filePath"
)
),
AddPsuStratificationVariables = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
StratificationVariables = "stratificationvariablesvector",
StratificationVariablesTable = "stratificationvariablestable"
)
),
AssignPSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
SamplingUnitId = "samplingunitid",
DataRecordId = "datarecordid"
),
functionParameterDefaults = list(
DefinitionMethod = "MissingAtRandom"
)
),
AnalyticalPSUEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPSUEstimateData",
functionParameterFormat = list(
Variables = "individualnumericvariables",
DomainVariables = "individualdomainvariables",
PSUDomainVariables = "psudomainvariables"
)
),
AnalyticalPopulationEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData"
),
AnalyticalRatioEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
WeightVariable = "IndividualRoundWeight"
),
functionParameterFormat = list(
WeightVariable = "weightvariableratioestimate",
StratificationVariables = "stratificationvariableslandings",
DomainVariables = "domainvariableslandings"
)
),
ExtendAnalyticalSamplingFrameCoverage = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
Method = "SetToStratum",
UnsampledStratum = "Out-of-frame"
),
functionParameterFormat = list(
StratificationVariables = "stratificationvariableslandings",
SourceStratum = "sourcestratum"
),
functionArgumentHierarchy = list(
SourceStratum = list(
Method = "SetToStratum"
)
)
),
InterpolateAnalyticalDomainEstimates = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
Method = "StratumMean",
Epsilon = 1e-4
),
functionParameterFormat = list(
DomainMarginVariables = "domainvariableslandings"
),
functionArgumentHierarchy = list(
Epsilon = list(
Method = "StratumMean"
)
)
),
AggregateAnalyticalEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
RetainStrata=character(),
AggregateStratumName="Aggregate"
),
functionParameterFormat = list(
RetainStrata = "retainstrata"
)
),
ListBioticDifference = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
FilterAgeLengthOutliersStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
FilterWeightLengthOutliersStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
PrepareRecaEstimate = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaData",
functionParameterFormat = list(
RandomEffects = "randomcovariates",
CarEffect = "carcovariate",
FixedEffects = "fixedcovariates"),
functionArgumentHierarchy = list(
AgeErrorMatrix = list(
UseAgingError = TRUE
),
CarNeighbours = list(
UseCarEffect = TRUE
),
CarEffect = list(
UseCarEffect = TRUE
),
StockSplittingParameters = list(
UseStockSplitting = TRUE
),
UseStockSplittingError = list(
UseStockSplitting = TRUE
)
),
functionParameterDefaults = list(
CellEffect = "Off",
HatchDay = 1
)
),
ParameterizeRecaModels = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaParameterData",
functionParameterDefaults = list(
Lgamodel = "log-linear",
Delta.age = 0.001,
Thin = 0
)
#doesnt work for directory ?
#functionParameterFormat = list(
# ResultDirectory = "filePath"
#)
),
RunRecaModels = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaCatchAtAge",
functionParameterFormat = list(
GroupingVariables = "GroupingVariables"
),
functionParameterDefaults = list(
TemporalResolution = "Quarter",
Caa.burnin = 0
)
),
ReportFdaSampling = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaSamplingData",
functionParameterFormat = list(
GroupingVariables = "samplereportvariables",
SamplingVariables = "onlysamplereportvariables"
),
functionParameterDefaults = list(
Decimals = 0,
Unit = "kg"
)
),
ReportFdaLandings = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLandingData",
functionParameterFormat = list(
GroupingVariables = "landingsreportvariables"
),
functionParameterDefaults = list(
Decimals = 0,
Unit = "kg"
)
),
ReportRecaCatchAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportAnalyticalCatchAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "individuals"
)
),
AddLengthGroupStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
LengthGroupVariable = "LengthGroup",
LeftOpen = TRUE
)
),
ReportAnalyticalCatchAtLength = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "individuals"
),
functionParameterFormat = list(
LengthGroupVariable = "domainvariablecatchatlength"
)
),
ReportAnalyticalWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaWeightAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "g"
)
),
ReportAnalyticalLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLengthAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "cm"
)
),
ReportRecaCatchAtAgeCovariance = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeCovarianceData",
functionParameterDefaults = list(
Decimals = 0
)
),
ReportRecaCatchAtLength = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportRecaCatchAtLengthAndAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthAndAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportRecaLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLengthAtAgeData",
functionParameterDefaults = list(
Decimals = 2,
IntervalWidth = 0.9,
Threshold=0
)
),
ReportRecaWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaWeightAtAgeData",
functionParameterDefaults = list(
Decimals = 2,
IntervalWidth = 0.9,
Threshold=0
)
),
ReportRecaCatchStatistics = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchSummaryData",
functionArgumentHierarchy = list(
DecimalTotalNumber = list(
UseDefaultDecimalOptions = FALSE
),
DecimalTotalWeight = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanAge = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanWeight = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanLength = list(
UseDefaultDecimalOptions = FALSE
),
UnitTotalNumber = list(
UseDefaultUnitOptions = FALSE
),
UnitTotalWeight = list(
UseDefaultUnitOptions = FALSE
),
UnitMeanWeight = list(
UseDefaultUnitOptions = FALSE
),
UnitMeanLength = list(
UseDefaultUnitOptions = FALSE
)
),
functionParameterDefaults = list(
DecimalTotalNumber = 0,
DecimalTotalWeight = 0,
DecimalMeanAge = 1,
DecimalMeanWeight = 3,
DecimalMeanLength = 3,
UnitTotalNumber = "10^6 individuals",
UnitTotalWeight = "kiloton",
UnitMeanWeight = "kg",
UnitMeanLength = "cm",
IntervalWidth = 0.9
)
),
ReportFdaSOP = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaSopData",
functionParameterFormat = list(
GroupingVariables = "GroupingVariablesSop"
),
functionParameterDefaults = list(
DecimalWeight = 0,
DecimalFraction = 3
)
),
ReportRecaParameterStatistics = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ParameterizationSummaryData",
functionArgumentHierarchy = list(
ParameterizationSummaryData = list(
AppendReport = TRUE
)
)
),
ReportParameterConvergence = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ParameterConvergenceData",
functionParameterDefaults = list(
Tolerance = 0.1,
Decimals = 2
)
),
PlotFisheriesOverviewSpatial = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotFisheriesOverviewSpatialData"
),
PlotFisheriesOverviewTemporal = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotFisheriesOverviewTemporalData"
),
PlotSamplingOverviewCell = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingOverviewCellData",
functionParameterFormat = list(
ColumnVariable = "columnvariablecellplot"
),
functionArgumentHierarchy = list(
MinVessels = list(
UseDefaultColorSettings = FALSE
),
MinCatches = list(
UseDefaultColorSettings = FALSE
),
MinMeasurements = list(
UseDefaultColorSettings = FALSE
),
ColorNoSamples = list(
UseDefaultColorSettings = FALSE
),
ColorFewMeasurements = list(
UseDefaultColorSettings = FALSE
),
ColorFewCatches = list(
UseDefaultColorSettings = FALSE
),
ColorFewVessels = list(
UseDefaultColorSettings = FALSE
),
ColorGoodSampling = list(
UseDefaultColorSettings = FALSE
)
),
functionParameterDefaults = c(
plotDefaultsCellPlotColors,
list(Measurement="AgeReadings")
)
),
PlotSamplingCoverage = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingCoverageData",
functionArgumentHierarchy = list(
Measurement = list(
ColorScheme = "CellPlot"
),
MinVessels = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
MinCatches = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
MinMeasurements = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorNoSamples = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewMeasurements = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewCatches = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewVessels = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorGoodSampling = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
SamplingUnit = list(
ColorScheme = "Gradient"
),
GradientLowColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
),
GradientMidColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
),
GradientHighColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
)
),
functionParameterDefaults = c(
plotDefaultsCellPlotColors,
list(
GradientLowColor="#ffffcc",
GradientMidColor="#c2e699",
GradientHighColor="#006837",
OtherPercentage=0,
ColorScheme = "CellPlot",
Measurement = "AgeReadings"
)
)
),
PlotSamplingVariables = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingVariablesData"
),
PlotPosteriorTraces = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotPosteriorTracesData",
functionArgumentHierarchy = list(
Nclust = list(
UseDefaultPlotSettings=FALSE
),
Iter.max = list(
UseDefaultPlotSettings=FALSE
),
Nstart = list(
UseDefaultPlotSettings=FALSE
),
LowerQuant = list(
UseDefaultPlotSettings=FALSE
),
UpperQuant = list(
UseDefaultPlotSettings=FALSE
),
CatLimit = list(
UseDefaultPlotSettings=FALSE
),
Legend = list(
UseDefaultPlotSettings=FALSE
)
),
functionParameterDefaults = list(
Nclust=4,
Iter.max=20,
Nstart=10,
LowerQuant=.05,
UpperQuant=.95,
CatLimit=30,
Parameter="TotalCatch"
)
),
PlotCatchAtAgeTotals = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotCatchAtAgeTotalsData"
),
PlotMeanWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotMeanWeightAtAgeData"
),
PlotMeanLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotMeanLengthAtAgeData"
)
)
#' Define the process property formats for inclusion in stox UI
#'
#' @export
#'
processPropertyFormats <- list(
filePath = list(
class = "single",
title = "The path to a single file"
),
periodvector = list(
class = "vector",
title = "Period defintinions. Start date on the form \"DD-MM\" or \"DD-MM-YYYY\"",
variableTypes = "character"
),
randomcovariates = list(
class = "vector",
title = "One or more variables to use as covariates in Reca",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[!(possibleValues %in% c("CruiseKey", "StationKey", "HaulKey", "SpeciesCategoryKey", "SampleKey"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
stratificationcolumns = list(
class = "vector",
title = "One or more variables to use as stratification columns",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualstratificationcolumns = list(
class = "vector",
title = "One or more variables to use as stratification columns",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualdomainvariables = list(
class = "vector",
title = "One or more variables to use for domain definitions",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- possibleValues[!(possibleValues) %in% c("CruiseKey", "HaulKey", "IndividualKey", "SampleKey", "SpeciesCategoryKey", "StationKey")]
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
psudomainvariables = list(
class = "vector",
title = "One or more variables to use for domain definitions",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- possibleValues[!(possibleValues) %in% c("CruiseKey", "HaulKey", "IndividualKey", "SampleKey", "SpeciesCategoryKey", "StationKey")]
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualparameters = list(
class = "vector",
title = "One or more parameter to included in the design specification",
possibleValues = function(StoxBioticData) {
possibleValues <- unique(names(StoxBioticData$Individual))
return(sort(possibleValues))
},
variableTypes = "character"
),
individualnumericvariables = list(
class = "vector",
title = "One or more numeric variables",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.numeric(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
},
variableTypes = "character"
),
samplingunitid = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable to use for sampling unit identification (choose only one)",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
datarecordid = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable to use for data record identification (choose only one)",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
weightvariableratioestimate = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable that represent weight of individuals in grams (choose only one)",
possibleValues = function(AnalyticalPopulationEstimateData) {
possibleValues <- unique(AnalyticalPopulationEstimateData$Variables$Variable)
return(sort(possibleValues))
},
variableTypes = "character"
),
GroupingVariablesSop = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(ReportFdaCatchAtAgeData, ReportFdaWeightAtAgeData, StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[(names(StoxLandingData$Landing) %in% ReportFdaCatchAtAgeData$GroupingVariables$GroupingVariables) &
(names(StoxLandingData$Landing) %in% ReportFdaWeightAtAgeData$GroupingVariables$GroupingVariables)]
return(sort(possibleValues))
},
variableTypes = "character"
),
GroupingVariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[!(names(StoxLandingData$Landing) %in% c("RoundWeight"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
samplereportvariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData, StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[possibleValues %in% names(StoxLandingData$Landing)]
return(sort(possibleValues))
},
variableTypes = "character"
),
onlysamplereportvariables = list(
class = "vector",
title = "One or more variables to use as sampling variables.",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in names(StoxBioticData)){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
landingsreportvariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[names(StoxLandingData$Landing) != "Rundvekt"]
return(sort(possibleValues))
},
variableTypes = "character"
),
fixedcovariates = list(
class = "vector",
title = "One or more variables to use as covariates in Reca",
possibleValues = function(StoxLandingData, StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[possibleValues %in% names(StoxLandingData$Landing)]
return(sort(possibleValues))
},
variableTypes = "character"
),
carcovariate = list(
class = "vector", #convert to class single, if that becomes available.
title = "A variable (choose only one) to use for the CAR variable in Reca.",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[!(possibleValues %in% c("CruiseKey", "StationKey", "HaulKey", "SpeciesCategoryKey", "SampleKey"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
columnvariablecellplot = list(
class = "vector", #convert to class single, if that becomes available.
title = "A variable (choose only one) to use for columns in cell plot.",
possibleValues = function(ReportFdaSamplingData) {
possibleValues <- ReportFdaSamplingData$GroupingVariables$GroupingVariables
return(sort(possibleValues))
},
variableTypes = "character"
),
stratificationvariableslandings = list(
class = "vector",
title = "One or more Stratificaion variable to match with landings",
possibleValues = function(AnalyticalPopulationEstimateData, StoxLandingData){
pv <- names(AnalyticalPopulationEstimateData$StratificationVariables)
pv <- pv[pv != "Stratum"]
pv <- pv[pv %in% names(StoxLandingData$Landing)]
return(pv)
},
variableTypes = "character"
),
domainvariableslandings = list(
class = "vector",
title = "Zero or more Domain variables to match with landings",
possibleValues = function(AnalyticalPopulationEstimateData, StoxLandingData){
pv <- names(AnalyticalPopulationEstimateData$DomainVariables)
pv <- pv[pv != "Domain"]
pv <- pv[pv %in% names(StoxLandingData$Landing)]
return(pv)
},
variableTypes = "character"
),
domainvariablecatchatlength = list(
class = "vector", #convert to class single, if that becomes available.
title = "Exactly on Domain variable to specify length group",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- names(AnalyticalPopulationEstimateData$DomainVariables)
pv <- pv[pv != "Domain"]
return(pv)
},
variableTypes = "character"
),
sourcestratum = list(
class = "vector", #convert to class single, if that becomes available.
title = "Exactly one stratum to use for sampling-frame extension",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum)
return(pv)
},
variableTypes = "character"
),
stratificationvariablesvector = list(
class = "vector",
title = "Names of stratification variables to add",
variableTypes = "character"
),
stratificationvariablestable = list(
class = "table",
title = "Table of Stratification variables for each Stratum",
columnNames = function(StratificationVariables) {
c("Stratum", StratificationVariables)
},
variableTypes = function(StratificationVariables) {
rep("character", 1 + length(StratificationVariables))
}
),
retainstrata = list(
class = "vector",
title = "Strata to retain from aggregation",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum)
return(pv)
},
variableTypes = "character"
),
collapsestrataretain = list(
class = "vector",
title = "Strata to retain from collapse",
possibleValues = function(IndividualSamplingParametersData){
pv <- names(IndividualSamplingParametersData$StratificationVariables)
pv <- pv[!(pv %in% c("Stratum", "SampleId"))]
return(pv)
},
variableTypes = "character"
)
)
StoXProject/RstoxFDA documentation built on June 14, 2025, 1:37 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions is.StockSplittingParameters is.AgeErrorMatrix is.CarNeighbours is.AreaPosition is.TemporalDefinition is.RecaResult is.RecaPrediction is.RecaCatchAtAge is.RecaParameterData is.RecaData is.ReportFdaSamplingData is.WeightConversionTable is.LengthConversionTable is.Translation is.ParameterConvergenceData is.ParameterizationSummaryData is.ReportFdaSOP is.ReportFdaData is.StoxBioticData is.MetierTable is.IndividualSamplingParametersData is.PSUSamplingParametersData is.AnalyticalPopulationEstimateData is.AnalyticalPSUEstimateData is.Date is.POSIXct
Documented in is.AgeErrorMatrix is.AreaPosition is.CarNeighbours is.LengthConversionTable is.ParameterConvergenceData is.ParameterizationSummaryData is.RecaCatchAtAge is.RecaData is.RecaParameterData is.RecaResult is.ReportFdaData is.ReportFdaSamplingData is.ReportFdaSOP is.StockSplittingParameters is.TemporalDefinition is.Translation is.WeightConversionTable
#' @noRd
is.POSIXct <- function(date){
if ("POSIXct" %in% class(date)){
return(TRUE)
}
return(FALSE)
}
#' @noRd
is.Date <- function(date){
if ("Date" %in% class(date)){
return(TRUE)
}
return(FALSE)
}
#' Analytical PSU Estimate Data
#'
#' @description
#' Analytical estimates for each PSU
#'
#' List containing the following \code{\link[data.table]{data.table}}s:
#'
#' Abundance
#' \describe{
#' \item{SampleId}{Identfier for Primary sampling unit (PSU)}
#' \item{Stratum}{Identifier for stratification of individuals}
#' \item{Domain}{Identifier of domains for individuals}
#' \item{Abundance}{Total number in Stratum and Domain at th PSU. Unsampled strata is reported as NA}
#' \item{Frequency}{Frequency in Domain within Stratum. Unsampled strata is reported as NA}
#' }
#'
#' Variables
#' \describe{
#' \item{SampleId}{Identfier for Primary sampling unit (PSU)}
#' \item{Stratum}{Identifier for stratification of individuals}
#' \item{Domain}{Identifier of domains for individuals}
#' \item{Variable}{Variable that total and mean is reported for}
#' \item{Total}{Total value of variable in Stratum and Domain at the PSU. Unsampled strata is reported as NA}
#' \item{Mean}{Mean value of variable in Stratum and Domain at the PSU. Unsampled strata is reported as NA}
#' }
#'
#' DomainVariables
#' \describe{
#' \item{Domain}{Identifier of domains for individuals. In addition the domain is identified by the combination of any additional columns in this table}
#' \item{<DomainVariables>}{Columns that relate the domains to data records.}
#' }
#'
#' PSUDomainVariables
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{PSUDomain}{Identfier of domains for PSUs. In addition PSU-domains are identified by the combination of any additional columns in this table.}
#' \item{<DomainVariables>}{Columns that relate the PSU domains to data records.}
#' }
#'
#' StratificationVariables
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{Stratum}{Identfier of stratum for individuals at PSU. In addition strata are identified by the combination of any additional columns in this table.}
#' \item{<StratificationVariables>}{Columns that relate the PSU domains to data records.}
#' }
#'
#' The combination of the columns 'Stratum' and 'SampleId' uniquely identifies a row in the table 'StratificationVariables'
#' The columns <StratificationVariables> are optional, but if present; their combination must identify a stratum for each 'SamplingId'.
#'
#' SampleCount
#' \describe{
#' \item{SampleId}{Identifier for Primary Sampling Unit (PSU)}
#' \item{Stratum}{Identfier of stratum for individuals at PSU. In addition strata are identified by the combination of any additional columns in this table.}
#' \item{Domain}{Identifier of domains for individuals. In addition the domain is identified by the combination of any additional columns in this table}
#' \item{nIndividuals}{Number of individuals observed for the domain}
#' }
#'
#' @name AnalyticalPSUEstimateData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if input is correctly formatted Analytical PSU Estimate Data
#' @param table \code{\link[RstoxFDA]{AnalyticalPSUEstimateData}}
#' @return validity
#' @concept Data types
#' @noRd
is.AnalyticalPSUEstimateData <- function(AnalyticalPSUEstimateData){
if (!is.list(AnalyticalPSUEstimateData)){
return(FALSE)
}
if (!all(c("Abundance", "Variables", "DomainVariables", "PSUDomainVariables", "StratificationVariables", "SampleCount") %in% names(AnalyticalPSUEstimateData))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "Abundance", "Frequency") %in% names(AnalyticalPSUEstimateData$Abundance))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "Variable", "Total", "Mean") %in% names(AnalyticalPSUEstimateData$Variables))){
return(FALSE)
}
if (!all(c("Domain") %in% names(AnalyticalPSUEstimateData$DomainVariables))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Domain", "nIndividuals") %in% names(AnalyticalPSUEstimateData$SampleCount))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum") %in% names(AnalyticalPSUEstimateData$StratificationVariables))){
return(FALSE)
}
if (any(!(names(AnalyticalPSUEstimateData$StratificationVariables) %in% c("Stratum", "SampleId")))){
stratavars <- names(AnalyticalPSUEstimateData$StratificationVariables)[!(names(AnalyticalPSUEstimateData$StratificationVariables) %in% c("Stratum", "SampleId"))]
stratacount <- AnalyticalPSUEstimateData$StratificationVariables[,list(stratacount=length(unique(paste(get("Stratum"))))), by=list(stratavars=apply(AnalyticalPSUEstimateData$StratificationVariables[,.SD,.SDcol=c(stratavars, "SampleId")], 1, paste, collapse="/"))]
if (any(stratacount$stratacount>1)){
return(FALSE)
}
if (any(is.na(AnalyticalPSUEstimateData$StratificationVariables[,.SD,.SDcols = stratavars]))){
return(FALSE)
}
}
if (any(duplicated(paste(AnalyticalPSUEstimateData$StratificationVariables$SampleId, AnalyticalPSUEstimateData$StratificationVariables$Stratum)))){
return(FALSE)
}
return(TRUE)
}
#' Analytical Population Estimate Data
#'
#' @description
#' Analytical estimates for a population
#'
#' Abundance
#' \describe{
#' \item{Stratum}{Stratum that abundance is provided for.}
#' \item{Domain}{The domain that the abundance is provided for.}
#' \item{Abundance}{The estimated number of individuals in the domain and stratum.}
#' \item{Frequency}{The estimated fraction of individuals in the domain, relative ot the total number in stratum.}
#' }
#'
#' Variables
#' \describe{
#' \item{Stratum}{The stratum that estimates are provided for.}
#' \item{Domain}{The domain that estimates are provided for.}
#' \item{Variable}{The variable (measurment) that estimates are provided for.}
#' \item{Total}{The estimated total value of the variable in domain and stratum.}
#' \item{Mean}{The estimated mean value fo the variable in domain and stratum.}
#' }
#'
#' AbundanceCovariance
#' \describe{
#' \item{Stratum}{The stratum that covariances are provided for.}
#' \item{Domain1}{A domain that covariances are provided for. Unique, given Stratum}
#' \item{Domain2}{A domain that covariances are provided for. Unique, given Stratum and Domain 1.}
#' \item{AbundanceCovariance}{The estimated covariance of abundance between Domain1 and Domain2.}
#' \item{FrequencyCovariance}{The estimated covariance of frequency between Domain1 and Domain2.}
#' }
#'
#' VariableCovariance
#' \describe{
#' \item{Stratum}{The stratum that covariances are provied for.}
#' \item{Domain1}{A domain that covariances are provided for. Unique, given Stratum}
#' \item{Domain2}{A domain that covariances are provided for. Unique, given Stratum and Domain 1.}
#' \item{Variable1}{A variable that covariances are provided for. Unique, given Stratum and domains.}
#' \item{Variable2}{A variable that covariances are provided for. Unique, given Stratum, domains and Variable1.}
#' \item{TotalCovariance}{The estimated covariance of total value of Variable1 in Domain1 and Variable2 in Domain2}
#' \item{MeanCovariance}{The estimated covariance of the mean value of Variable1 in Domain1 and Variable2 in Domain2}
#' }
#'
#' StratificationVariables
#' \describe{
#' \item{Stratum}{A stratum, as identified in other tables.}
#' \item{<StratificationVariables>}{Columns that relate the stratum to data records.}
#' }
#' The columns <StratificationVariables> are optional, but if present; their combination must identify a stratum.
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' Unlike \code{\link[RstoxFDA]{PSUSamplingParametersData}}, \code{\link[RstoxFDA]{AnalyticalPSUEstimateData}}, and, \code{\link[RstoxFDA]{IndividualSamplingParametersData}}, a stratum for 'AnalyticalPopulationEstimateData'
#' can be defined by several rows in the 'StratificationVariables' table.
#'
#' DomainVariables
#' \describe{
#' \item{Domain}{A domain, as identified in other tables.}
#' \item{DomainVariables}{Columns that relate the domain to data records.}
#' }
#'
#' SampleCount
#' \describe{
#' \item{Stratum}{A stratum, as identified in other tables.}
#' \item{Domain}{A domain, as identified in other tables.}
#' \item{nPSU}{The number of Primary Sampling Units observed for this domain in this stratum}
#' \item{nIndividuals}{The number of individuals observed for this domain in this stratum}
#' }
#'
#' @name AnalyticalPopulationEstimateData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if input is correctly formatted Analytical Population Estimate Data
#' @param table \code{\link[RstoxFDA]{AnalyticalPopulationEstimateData}}
#' @return validity
#' @concept Data types
#' @noRd
is.AnalyticalPopulationEstimateData <- function(AnalyticalPopulationEstimateData){
if (!is.list(AnalyticalPopulationEstimateData)){
return(FALSE)
}
if (!all(c("Abundance", "Variables", "AbundanceCovariance", "VariablesCovariance", "DomainVariables", "StratificationVariables", "SampleCount") %in% names(AnalyticalPopulationEstimateData))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "Abundance", "Frequency") %in% names(AnalyticalPopulationEstimateData$Abundance))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "Variable", "Total", "Mean") %in% names(AnalyticalPopulationEstimateData$Variables))){
return(FALSE)
}
if (!all(c("Stratum", "Domain1", "Domain2", "Variable1", "Variable2", "TotalCovariance", "MeanCovariance") %in% names(AnalyticalPopulationEstimateData$VariablesCovariance))){
return(FALSE)
}
if (!all(c("Stratum", "Domain1", "Domain2", "AbundanceCovariance", "FrequencyCovariance") %in% names(AnalyticalPopulationEstimateData$AbundanceCovariance))){
return(FALSE)
}
if (!all(c("Domain") %in% names(AnalyticalPopulationEstimateData$DomainVariables))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(AnalyticalPopulationEstimateData$StratificationVariables))){
return(FALSE)
}
if (!all(c("Stratum", "Domain", "nPSU", "nIndividuals") %in% names(AnalyticalPopulationEstimateData$SampleCount))){
return(FALSE)
}
domains <- nrow(AnalyticalPopulationEstimateData$DomainVariables) * length(unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum))
if (domains != nrow(AnalyticalPopulationEstimateData$Abundance)){
return(FALSE)
}
if (domains*length(unique(AnalyticalPopulationEstimateData$Variables$Variable)) != nrow(AnalyticalPopulationEstimateData$Variables)){
return(FALSE)
}
if (any(names(AnalyticalPopulationEstimateData$StratificationVariables != "Stratum"))){
stratavars <- names(AnalyticalPopulationEstimateData$StratificationVariables)[names(AnalyticalPopulationEstimateData$StratificationVariables)!="Stratum"]
stratvarString <- apply(AnalyticalPopulationEstimateData$StratificationVariables[,.SD,.SDcols = stratavars],
1, paste, collapse="/")
if (any(duplicated(stratvarString))){
return(FALSE)
}
if (any(is.na(AnalyticalPopulationEstimateData$StratificationVariables[,.SD,.SDcols = stratavars]))){
return(FALSE)
}
}
#check that strata is reported completely and consistently
for (nn in names(AnalyticalPopulationEstimateData)){
if ("Stratum" %in% names(AnalyticalPopulationEstimateData[[nn]])){
if (!all(AnalyticalPopulationEstimateData[[nn]]$Stratum
%in% AnalyticalPopulationEstimateData$StratificationVariables$Stratum)){
return(FALSE)
}
if (!all(AnalyticalPopulationEstimateData$StratificationVariables$Stratum %in%
AnalyticalPopulationEstimateData[[nn]]$Stratum)){
return(FALSE)
}
}
}
#check that domains are reported consistently
for (nn in names(AnalyticalPopulationEstimateData)){
if ("Domain" %in% names(AnalyticalPopulationEstimateData[[nn]])){
if (!all(AnalyticalPopulationEstimateData[[nn]]$Domain
%in% AnalyticalPopulationEstimateData$DomainVariables$Domain)){
return(FALSE)
}
}
}
return(TRUE)
}
#' PSU Sampling Design Parameters
#'
#' Sampling parameters for selection of Primary Sampling Units
#'
#' @details
#' Encodes information about the selection of Primary Sampling Units in multi-stage sampling, used in analytical design based estimation.
#' Information is encoded in three tables.
#'
#' The SampleTable encodes information about the sample of sampling units:
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum the sample is taken from. Treat unstratified sample as single-stratum sampling (provide only one stratum.}
#' \item{N}{Optional, num: The total number of PSUs in Stratum (total available for selection, not total selected)}
#' \item{n}{Optional, num: The number of PSUs selected from the Stratum}
#' \item{SelectionMethod}{Mandatory, chr: 'Poission', 'FSWR' or 'FSWOR'. The manner of selection for use in bootstrap or inference of inclusionProbabilities, selectionProbabilites, co-inclusion probabilities or co-selection probabilities.}
#' \item{FrameDescription}{Optional, chr: Free text field describing the sampling frame.}
#' }
#'
#' The SelectionTable encodes information abut the selection of sampling units for sampling:
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum the PSU is taken from.}
#' \item{Order}{Optional, num: Identifes the order of seleciton. May be necessary for inference when selections are not independent (e.g. FSWOR)}
#' \item{SamplingUnitId}{Optional, chr: Identifes PSU. NA encodes non-response}
#' \item{InclusionProbability}{Optional, num: The inclusion probability of the PSU}
#' \item{HTsamplingWeight}{Optional, num: The normalized Horvitz-Thompson sampling weight of the PSU}
#' \item{SelectionProbability}{Optional, num: The selection probability of the PSU}
#' \item{HHsamplingWeight}{Optional, num: The normalized Hansen-Hurwitz sampling weight of the PSU}
#' \item{SelectionDescription}{Optional, chr: Free text description of the PSU.}
#' }
#'
#' The StratificationVariables table encodes information about which columns in the sampleTable are stratification variables (if any):
#' \describe{
#' \item{Stratum}{Mandatory, chr: Identifies the stratum. In addition the Stratum is identified by the combination of all other columns on this table.}
#' \item{<StratificationVariables>}{Mandatory if present (may not contain NAs), chr: Additional columns in the sampleTable that are stratification variables.}
#' }
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' The column 'Stratum' uniquely identifies a row in the table 'StratificationVariables'
#'
#' Optional columns may be NA.
#'
#' The selection methods available for 'SelectionMethod' are explained here:
#' \describe{
#' \item{Poission}{Poission sampling. Selection is performed randomly without replacement, and each selection is performed individually. Sample size is not fixed, and 'n' represents the expected sample size.}
#' \item{FSWR}{Fixed sample size with replacement. A random selection of a fixed sample size 'n' is chosen with replacement}
#' \item{FSWOR}{Fixed sample size without replacement. A random selection of a fixed sample size 'n' is chosen without replacement. Order of selection could be specified in the 'selectionTable'}
#' }
#'
#' \describe{
#' \item{The SelectionProbability is defined as:}{The probability of selecting the sampling unit when it was selected from the population.}
#' \item{The HHsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sampled unit when estimating with the Hansen-Hurwitz strategy: 1 / (SelectionProbability*Q) , where Q is the sum of the reciprocal of the SelectionProbabilites for the sampled units. For equal probability sampling with replacement, this is simply 1/n, where n i sample size.}
#' \item{The InclusionProbability is defined as:}{The probability of the sampling unit being included in the sample.}
#' \item{The HTsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sample when estimating with the Horvitz-Thompson strategy: 1 / (InclusionProbability*P), where P is the sum of the reciprocal of the InclusionProbabilites for the sampled units. For equal probability sampling without replacement, this is simply 1/n, where n is sample size.}
#' }
#'
#' @name PSUSamplingParametersData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if table is correctly formatted PSU Sampling Parameters Data
#' @param table \code{\link[RstoxFDA]{PSUSamplingParametersData}}
#' @return validity
#' @concept Data types
#' @noRd
is.PSUSamplingParametersData <- function(PSUSamplingParametersData){
if (!is.list(PSUSamplingParametersData)){
return(FALSE)
}
if (!all(sapply(PSUSamplingParametersData, data.table::is.data.table))){
return(FALSE)
}
if (!all(c("SampleTable", "SelectionTable", "StratificationVariables") %in% names(PSUSamplingParametersData))){
return(FALSE)
}
if (!all(c("Stratum", "N", "n", "SelectionMethod", "FrameDescription") %in% names(PSUSamplingParametersData$SampleTable))){
return(FALSE)
}
if (!all(c("Stratum", "Order", "SamplingUnitId", "InclusionProbability", "SelectionProbability", "HHsamplingWeight", "SelectionDescription") %in% names(PSUSamplingParametersData$SelectionTable))){
return(FALSE)
}
if (any(duplicated(PSUSamplingParametersData$SelectionTable$SamplingUnitId[!is.na(PSUSamplingParametersData$SelectionTable$SamplingUnitId)]))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(PSUSamplingParametersData$StratificationVariables))){
return(FALSE)
}
if (any(duplicated(PSUSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
#test that mandatory fields are not NA.
if (any(is.na(PSUSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$SampleTable$SelectionMethod))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$SelectionTable$Stratum))){
return(FALSE)
}
if (any(is.na(PSUSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
for (n in names(PSUSamplingParametersData$StratificationVariables)){
if (any(is.na(PSUSamplingParametersData$StratificationVariables[[n]]))){
return(FALSE)
}
}
if (ncol(PSUSamplingParametersData$StratificationVariables) > 1){
stratificationVariableStrings <- apply(PSUSamplingParametersData$StratificationVariables[,.SD, .SDcol=names(PSUSamplingParametersData$StratificationVariables)[names(PSUSamplingParametersData$StratificationVariables)!="Stratum"]], 1, paste, collapse="/")
duplicatedStrata <- PSUSamplingParametersData$StratificationVariables$Stratum[duplicated(stratificationVariableStrings)]
if (length(duplicatedStrata)>0){
return(FALSE)
}
}
if (any(duplicated(PSUSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
return(TRUE)
}
#' Individual Sub-Sampling Design Parameters
#'
#' Sampling parameters for selection of a sub-sample of individuals
#'
#' @details
#' Encodes information about the selection of a sub-sample of observations from individuals, used in analytical design based estimation.
#' A sub-sample is simply a sample of a sample. This data type is intended to represent the final stage of sampling in multi-stage sampling,
#' and therefore has a reference to the Sample it was taken from ('SampleId'). Apart from that there is no principal difference from single
#' stage sampling. All stratification is specified within the sample identifed by 'SampleId', and all sampling probabilites are specified within strata.
#'
#' The SampleTable encodes information about the sample of sampling units:
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the sub-sample is taken from.}
#' \item{Stratum}{Mandatory, chr: Identifies the within-sample stratum the sub-sample is taken from. Treat unstratified sample as single-stratum sampling (provide only one stratum. All strata with strata size > 0 must be reported for each SampleId.}
#' \item{N}{Optional, num: The total number of individuals in Stratum. For unstratified sampling, the total number of individuals in the sample the sub-sample is taken from.}
#' \item{n}{Optional, num: The number of individuals selected from the Stratum}
#' \item{SelectionMethod}{Mandatory, chr: 'Poission', 'FSWR' or 'FSWOR'. The manner of selection for use in bootstrap or inference of inclusionProbabilities, selectionProbabilites, co-inclusion probabilities or co-selection probabilities.}
#' \item{SampleDescription}{Optional, chr: Free text field describing the sample that is subsampled.}
#' }
#'
#' The SelectionTable encodes information abut the selection of sampling units for sampling:
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the sub-sample is taken from.}
#' \item{Stratum}{Mandatory, chr: Identifies the within sample-stratum the individual is taken from.}
#' \item{Order}{Optional, num: Identifes the order of seleciton. May be necessary for inference when selections are not independent (e.g. FSWOR)}
#' \item{IndividualId}{Optional, chr: Identifes individual. NA encodes non-response / observation failure}
#' \item{InclusionProbability}{Optional, num: The inclusion probability of the individual}
#' \item{HTsamplingWeight}{Optional, num: The normalized Horvitz-Thompson sampling weight of the individual}
#' \item{SelectionProbability}{Optional, num: The selection probability of the individual}
#' \item{HHsamplingWeight}{Optional, num: The normalized Hansen-Hurwitz sampling weight of the individual}
#' \item{SelectionDescription}{Optional, chr: Free text description of sampling unit.}
#' }
#'
#' The StratificationVariables table encodes information about which columns in the sampleTable are stratification variables (if any):
#' \describe{
#' \item{SampleId}{Mandatory, chr: Identifies the sample the stratification applies to}
#' \item{Stratum}{Mandatory, chr: Identifies the within-sample stratum. In addition the Stratum is identified by the combination of all other columns on this table.}
#' \item{<StratificationVariables>}{Mandatory if present (may not contain NAs), chr: Additional columns in the sampleTable that are stratification variables.}
#' }
#' The Stratification Variables assist in matching each strata to census data, such as landings. See for instance \code{\link[RstoxFDA]{AnalyticalRatioEstimate}}
#' The combination of the columns 'Stratum' and 'SampleId' uniquely identifies a row in the table 'StratificationVariables'
#'
#' Optional columns may be NA.
#'
#' The selection methods available for 'SelectionMethod' are explained here:
#' \describe{
#' \item{Poission}{Poission sampling. Selection is performed randomly without replacement, and each selection is performed individually. Sample size is not fixed, and 'n' represents the expected sample size.}
#' \item{FSWR}{Fixed sample size with replacement. A random selection of a fixed sample size 'n' is chosen with replacement}
#' \item{FSWOR}{Fixed sample size without replacement. A random selection of a fixed sample size 'n' is chosen without replacement. Order of selection should be specified in the 'selectionTable'}
#' }
#'
#' \describe{
#' \item{The SelectionProbability is defined as:}{The probability of selecting the sampling unit when it was selected from the population.}
#' \item{The HHsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sampled unit when estimating with the Hansen-Hurwitz strategy: 1 / (SelectionProbability*Q) , where Q is the sum of the reciprocal of the SelectionProbabilites for the sampled units. For equal probability sampling with replacement, this is simply 1/n, where n i sample size.}
#' \item{The InclusionProbability is defined as:}{The probability of the sampling unit being included in the sample.}
#' \item{The HTsamplingWeight:}{The normalized sampling weight, or the fraction of the stratum represented by the sample when estimating with the Horvitz-Thompson strategy: 1 / (InclusionProbability*P), where P is the sum of the reciprocal of the InclusionProbabilites for the sampled units. For equal probability sampling without replacement, this is simply 1/n, where n is sample size.}
#' }
#'
#' @name IndividualSamplingParametersData
#' @concept Data types
#' @concept Analytical estimation
#'
NULL
#' Check if table is correctly formatted Individual Sampling Parameters Data
#' @param table \code{\link[RstoxFDA]{IndividualSamplingParametersData}}
#' @return validity
#' @concept Data types
#' @noRd
is.IndividualSamplingParametersData <- function(IndividualSamplingParametersData){
if (!is.list(IndividualSamplingParametersData)){
return(FALSE)
}
if (!all(sapply(IndividualSamplingParametersData, data.table::is.data.table))){
return(FALSE)
}
if (!all(c("SampleTable", "SelectionTable", "StratificationVariables") %in% names(IndividualSamplingParametersData))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "N", "n", "SelectionMethod", "SampleDescription") %in% names(IndividualSamplingParametersData$SampleTable))){
return(FALSE)
}
if (!all(c("SampleId", "Stratum", "Order", "IndividualId", "InclusionProbability", "HTsamplingWeight", "SelectionProbability", "HHsamplingWeight", "SelectionDescription") %in% names(IndividualSamplingParametersData$SelectionTable))){
return(FALSE)
}
if (!all(c("Stratum") %in% names(IndividualSamplingParametersData$StratificationVariables))){
return(FALSE)
}
if (any(duplicated(paste(IndividualSamplingParametersData$SampleTable$Stratum, IndividualSamplingParametersData$SampleTable$SampleId)))){
return(FALSE)
}
#test that mandatory fields are not NA.
if (any(is.na(IndividualSamplingParametersData$SampleTable$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SampleTable$SampleId))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SampleTable$SelectionMethod))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SelectionTable$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$SelectionTable$SampleId))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$StratificationVariables$Stratum))){
return(FALSE)
}
if (any(is.na(IndividualSamplingParametersData$StratificationVariables$SampleId))){
return(FALSE)
}
for (n in names(IndividualSamplingParametersData$StratificationVariables)){
if (any(is.na(IndividualSamplingParametersData$StratificationVariables[[n]]))){
return(FALSE)
}
}
if (ncol(IndividualSamplingParametersData$StratificationVariables) > 2){
stratificationVariableStrings <- apply(IndividualSamplingParametersData$StratificationVariables[,.SD, .SDcol=names(IndividualSamplingParametersData$StratificationVariables)[!(names(IndividualSamplingParametersData$StratificationVariables) %in% c("Stratum"))]], 1, paste, collapse="/")
duplicatedStrata <- IndividualSamplingParametersData$StratificationVariables$Stratum[duplicated(stratificationVariableStrings)]
if (length(duplicatedStrata)>0){
return(FALSE)
}
}
return(TRUE)
}
#' Trip Partition
#'
#' Partitioning of catch from a trip.
#'
#' @details
#' list with two members 'fractions' and 'groupDefinition'
#'
#' 'fractions' is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{tripid}{trip identifier}
#' \item{species}{species identifier}
#' \item{groupid}{identifies the group the fraction is provided for. Groups are further specified in 'groupDefinition'}
#' \item{fraction}{fraction of the total catch of species in the given group}
#' }
#'
#' 'groupDefinition' is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{groupid}{identifies the group the defintion is provided for.}
#' \item{...}{one or more custom columns definint the group}
#' }
#'
#' @name TripPartition
#' @concept Data types
#'
NULL
#' Metier table
#'
#' Table (\code{\link[data.table]{data.table}}) defining metiers.
#'
#' Fishing activity metiers are approximate decompositions of fleets commonly used in Europe.
#' This table defines an approximate definition of metiers by a custom gear code.
#'
#' Metiers are used in EU-regulations for EU-member states, and are therefore often required by ICES data formats
#' Metiers are formal strings encoding decomposition an idealized fleet or set of trips, where target species and gear are clearly and unambigiously identified.
#' The metier system recognize that these parameters are not always clearly and unamibigiously identified to the desired resolution, and allows for grouping and omission of some parameters, and for coding that information is missing.
#' This makes the metier system very flexible, but also provides poor standardization and ICES databases and data-calls may provide code-lists of allowed metiers, sometimes with different metiers being requested for different areas.
#' In effect metier annotations cannot be completely standardized, but must to be configurable through conversion tables like this. Often the annotation has to be approximate.
#'
#' The metier system is spesified by the EU - Data Collection Framework: \url{https://datacollection.jrc.ec.europa.eu/wordef/fishing-activity-metier},
#' briefly a metier is fully specified by a string: <gear>_<target species>_<gear mesh size>_<selectivity device>_<selectivity device mesh size>_<vessel length class>,
#' with coding systems and grouping conventions defined for each of the variables.
#' Common trunctations of these strings are also used. E.g.:
#' Metier level 6 (no truncation): <gear>_<target species>_<gear mesh size>_<selectivity device>_<selectivity device mesh size>
#' Metier level 5 : <gear>_<target species>
#' Metier level 4 : <gear>
#' The term "metier" is also used for some derived systems, for instance some data-calls requests that a code for intended usage of catch (industrial vs human consumption) be appended to the metiers where gear and target species is missing.
#'
#' For example, in intercatch, the code OTB_DEF_>=120_0_0_all identifies bottom trawl with otter boards (OTB) targeting demershal fish (DEF), with mesh size larger than or equal to 120 mm, no selectivity device (0_0) and all vessel size (all)
#'
#' @details
#' \describe{
#' \item{metier}{character() metier-string, e.g.: OTB_DEF_>=120_0_0_all}
#' \item{gearcode}{character() encoding gear}
#' \item{target}{character(), optional, target species}
#' \item{meshedGear}{logical(), optional, whether the gear is a meshed gear. Should be provided for all or none gears.}
#' \item{lowerMeshSize}{integer(), optional, the lower mesh size to include in this metier. Should be provided for all rows where meshedGear is True, and not for other rows.}
#' \item{upperMeshSize}{integer(), optional, the upper mesh size to include in this metier. Should be provided for all rows where meshedGear is True, and not for other rows.}
#' \item{selectivityDevice}{character(), optional, encoding selectivity device.}
#' \item{meshedSelectivityDevice}{logical(), optional, encoding selectivity device. Should be provided for all or none selectivity devices.}
#' \item{selDevLowerMeshSize}{integer(), optional, the lower mesh size of selectivity device to include in this metier. Should be provided for all rows where meshedSelectivityDevice is True, and not for other rows.}
#' \item{selDevUpperMeshSize}{integer(), optional, the upper mesh size of selectivity device to include in this metier. Should be provided for all rows where meshedSelectivityDevice is True, and not for other rows.}
#' }
#'
#' The metier-defining parameters are written in camelCase, parameters that may be used to distinguish applicability of different metierdefinitions are writter in UPPER case.
#'
#' @name MetierTable
#' @concept Data types
#'
NULL
#' Check if table is correctly formatted metier table
#' @param table \code{\link[RstoxFDA]{MetierTable}}
#' @param throwError if set errors are raised, if not, validity will be returned as T/F
#' @return validity
#' @concept Data types
#' @noRd
is.MetierTable <- function(table, throwError=F){
if (!data.table::is.data.table(table)){
if (throwError){
stop("A metiertable must be a data.table")
}
return(FALSE)
}
if (!all(c("metier", "gearcode", "target", "meshedGear", "lowerMeshSize", "upperMeshSize", "selectivityDevice", "meshedSelectivityDevice", "selDevLowerMeshSize", "selDevUpperMeshSize") %in% names(table))){
if (throwError){
stop("Metiertable does not have the required columns")
}
return(FALSE)
}
if (!is.character(table$metier)){
if (throwError){
stop("The column 'metier' must be a character")
}
return(FALSE)
}
if (!is.character(table$gearcode)){
if (throwError){
stop("The column 'gearcode' must be a character")
}
return(FALSE)
}
if (!is.logical(table$meshedGear)){
if (throwError){
stop("The column 'meshedGear' must be a logical")
}
return(FALSE)
}
if (!is.numeric(table$lowerMeshSize)){
if (throwError){
stop("The column 'lowerMeshSize' must be an integer")
}
}
if (!is.numeric(table$upperMeshSize)){
if (throwError){
stop("The column 'upperMeshSize' must be an integer")
}
return(FALSE)
}
if (!is.character(table$selectivityDevice)){
if (throwError){
stop("The column 'selectivityDevice' must be a character")
}
return(FALSE)
}
if (!is.logical(table$meshedSelectivityDevice)){
if (throwError){
stop("The column 'meshedSelectivityDevice' must be a logical")
}
return(FALSE)
}
if (!is.numeric(table$selDevLowerMeshSize)){
if (throwError){
stop("The column 'selDevLowerMeshSize' must be an integer")
}
}
if (!is.numeric(table$selDevUpperMeshSize)){
if (throwError){
stop("The column 'selDevUpperMeshSize' must be an integer")
}
return(FALSE)
}
meshed <- table$meshedGear[!is.na(table$gearcode)]
if (any(is.na(meshed)) & !all(is.na(meshed))){
if (throwError){
stop("The column 'meshedGear' has a value for some gears, but not all")
}
return(FALSE)
}
upperMesh <- table$upperMeshSize[!is.na(table$meshedGear) & table$meshedGear]
if (any(is.na(upperMesh))){
if (throwError){
stop("The column 'upperMeshSize' is not provided for all meshed gears (where meshedGear is True)")
}
return(FALSE)
}
lowerMesh <- table$lowerMeshSize[!is.na(table$meshedGear) & table$meshedGear]
if (any(is.na(lowerMesh))){
if (throwError){
stop("The column 'lowerMeshSize' is not provided for all meshed gears (where meshedGear is True)")
}
return(FALSE)
}
if (any((!is.na(table$lowerMeshSize) | !is.na(table$upperMeshSize)) & (is.na(table$meshedGear) | !table$meshedGear))){
if (throwError){
stop("Mesh sizes provided for gears that are not meshed (where meshedGear is missing or False)")
}
return(FALSE)
}
meshedSel <- table$meshedSelectivityDevice[!is.na(table$gearcode) & !is.na(table$selectivityDevice)]
if (any(is.na(meshedSel)) & !all(is.na(meshedSel))){
if (throwError){
stop("The column 'meshedSelectivityDevice' has a value for some gears, but not all")
}
return(FALSE)
}
if (any((!is.na(table$lowerMeshSize) | !is.na(table$upperMeshSize)) & is.na(table$gear))){
if (throwError){
stop("Mesh sizes provided for gear where 'gear' is not given")
}
return(FALSE)
}
upperMeshSD <- table$selDevUpperMeshSize[!is.na(table$meshedSelectivityDevice) & table$meshedSelectivityDevice]
if (any(is.na(upperMeshSD))){
if (throwError){
stop("The column 'selDevUpperMeshSize' is not provided for all meshed selectivty devices gears (where meshedSelectivityDevice is True)")
}
return(FALSE)
}
lowerMeshSD <- table$selDevLowerMeshSize[!is.na(table$meshedSelectivityDevice) & table$meshedSelectivityDevice]
if (any(is.na(lowerMeshSD))){
if (throwError){
stop("The column 'selDevUpperMeshSize' is not provided for all meshed selectivty devices gears (where meshedSelectivityDevice is True)")
}
return(FALSE)
}
if (any((!is.na(table$selDevLowerMeshSize) | !is.na(table$selDevUpperMeshSize)) & (is.na(table$meshedSelectivityDevice) | !table$meshedSelectivityDevice))){
if (throwError){
stop("Mesh sizes provided for selectivity devices that are not meshed (where meshedSelectivityDevice is missing or False)")
}
return(FALSE)
}
if (any((!is.na(table$selDevLowerMeshSize) | !is.na(table$selDevUpperMeshSize)) & is.na(table$selectivityDevice))){
if (throwError){
stop("Mesh sizes provided for selectivity devices where 'selectivityDevice' is not given")
}
return(FALSE)
}
return(TRUE)
}
#' Check if input conforms to StoxBioticData.
#' Should perhaps be moved to RstoxData together with is.StoxLandingData
#' @noRd
is.StoxBioticData <- function(StoxBioticData, raiseErrors=F){
if (!is.list(StoxBioticData)){
return(FALSE)
}
if (!all(sapply(StoxBioticData, FUN=data.table::is.data.table))){
return(FALSE)
}
if (!all(c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample", "Individual") %in% names(StoxBioticData))){
return(FALSE)
}
if (any(duplicated(StoxBioticData$Cruise$Cruise))){
if (raiseErrors){
stop("Duplicate Cruise keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Station$Station))){
if (raiseErrors){
stop("Duplicate Station keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Haul$Haul))){
if (raiseErrors){
stop("Duplicate Haul keys")
}
return(FALSE)
}
# species category only have component key
if (any(duplicated(paste(StoxBioticData$SpeciesCategory$CruiseKey, StoxBioticData$SpeciesCategory$StationKey, StoxBioticData$SpeciesCategory$HaulKey, StoxBioticData$SpeciesCategory$SpeciesCategory)))){
if (raiseErrors){
stop("Duplicate SpeciesCategory keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Sample$Sample))){
if (raiseErrors){
stop("Duplicate Sample keys")
}
return(FALSE)
}
if (any(duplicated(StoxBioticData$Individual$Individual))){
if (raiseErrors){
stop("Duplicate Individual keys")
}
return(FALSE)
}
return(TRUE)
}
#' LOCODE table (LocodeTable)
#'
#' @description
#' Table of United Nations Codes for Trade and Transport Locations (UN/LOCODE)
#'
#' \describe{
#' \item{Country}{ISO 3166 alpha-2 Country Code for the country in which the location is placed.}
#' \item{Location}{3-character code identifying the location within country}
#' \item{NameWoDiacritics}{Name of location without diacritic signs.}
#' \item{Subdivision}{ISO 1-3 character alphabetic and/or numeric code for the administrative division of the country concerned (the latter part of the complete ISO 3166-2 code element (after the hyphen).)}
#' \item{Status}{code for status of the code. E.g. Codes starting with A are various official approvals.}
#' \item{Function}{string with codes classifying the function of the location. If this contains 1, the location has a seaport.}
#' \item{Coordinates}{latitude and longitude in degrees and minutes.}
#' \item{Remarks}{general remark about location or change in coding}
#' \item{latitude}{latitude in decimal degrees}
#' \item{longitude}{longitude in decimal degrees}
#' }
#'
#'
#' @name LocodeTable
#' @concept Data types
#'
NULL
#' Kommune polygons
#'
#' @description
#' \code{\link[sf]{sf}} data.frame with area names identified in the column 'StratumName'. See \code{\link[RstoxBase]{StratumPolygon}}.
#'
#' Polygons are defined in WGS84 coordinates (unprojected).
#'
#' The data contains the following columns with rows for each area:
#' \describe{
#' \item{id}{Offical code for the muncipality (kommunenummer)}
#' \item{name}{Official name for the muncipality}
#' \item{StratumName}{Equal to id}
#' }
#'
#' @name KommunePolygons
#' @concept Data types
#' @import RstoxBase
#'
NULL
#' Landings archive (LandingsArchiveData)
#'
#' @description
#' Landings (aggregated sales notes). Format used for landings archive delivered to IMR by FDIR.
#' Additional documentation (in Norwegian) can be found in the files:
#' docs/documentation_landingsdata_archive_norwegian.csv
#' docs/selected_code_lists_landingsdata_archive_norwegian.xlsx
#' docs/supplementary_documentation_landingsdata_archive_norwegian.csv
#'
#' Note that 'AAR' and 'AAR2' denotes the year of catch, which may differ from the year of landing. The convention has been adopted to use 13 for 'LEVMND' (month of landing), when catch has been landed in January the year after catch.
#' \describe{
#' \item{AAR}{integer. Two last digits of year of catch}
#' \item{AAR2}{integer. Year of catch (4 digits)}
#' \item{FARTLAND}{character. Code for vessel flag (NOR for Norwegian, UTL for non-Norwegian)}
#' \item{LEVAAR}{integer. Two last digits of year of landing)}
#' \item{LEVMND}{integer. Month of landing (1=January, 12=December, 13=January following year)). Values larger than 13 are likely errors.}
#' \item{KYST}{integer. Code for whether the catch was caught within the coastal region (12 nautical miles from the coast). Code 0 denotes oceanic catch, code 8 and 9 denotes coastal catch.)}
#' \item{HOMR}{character. Main area of catch. As identified by the column 'StratumName' in \code{\link[RstoxFDA]{mainareaFdir2017}}), except that leading zeroes are not used for the areas 0-9.}
#' \item{LOK}{character. Location of catch. As identified by the column 'Lokasjon' in \code{\link[RstoxFDA]{locationsFdir2017}}), together with Main area (the column HAVOMR), except neither use leading zeroes.}
#' \item{REDS}{character. Gear code as defined by the standard NS9400. See code lists.}
#' \item{LEVHERRD}{character. Common official code for the muncipality (kommune) the catch was landed. Not using leading zeroes}
#' \item{LEVHERRD2}{character. Common official code for the muncipality (kommune) the catch was landed. Using leading zeroes}
#' \item{LEVFYLKE}{character. Common official code for the muncipality (fylke) the catch was landed. Same as the two leading digits in LEVHERRD2. Using leading zeroes}
#' \item{FISK}{character. Code for the species landed}
#' \item{FISK_NAVN}{character. Norwegian name of the species landed.}
#' \item{BIPROD}{character. Code for the product landed (0 codes for main-product, 1-8 codes for bi-products.)}
#' \item{ANVEND}{character. Code for the usage of the landing (human consumption vs industiral usage). See code lists.)}
#' \item{UTBET}{numeric. The prize paid to fisher in Norwegian currency at the time of purchase. Consult supplementary documentation for details.}
#' \item{VEKT}{numeric. Live weight (Round weight) of landed catch in kg. Listed as zero for bi-products.}
#' }
#'
#'
#' @name LandingsArchiveData
#' @concept Data types
#'
NULL
#' Logbooks (LstLogbookData)
#'
#' @description
#' Logbooks read from the lst format delivered by Directorate of Fisheries (FDIR).
#'
#' This format is not matched with WMS records and contains less detail than the format read by
#' \code{\link[RstoxData]{readErsFile}}
#'
#' Each row represent one fishing operation, which is defined in the legislation current at the time of reporting.
#'
#' Additional documentation (in Norwegian) can be found in the file:
#' doc/documentation_logbookdata_lst.xls
#' \describe{
#' \item{FAAR}{character. Year of catch (4 digits)}
#' \item{REGM}{character License number of fishing vessel (registreringsmerke)}
#' \item{FM}{character. Month of catch. number with leading zeroes. 1=January, 12=December}
#' \item{FD}{character. Day of catch. number with leading zeroes}
#' \item{DBNR}{character. Logbook number.}
#' \item{TUR}{character. Trip number.}
#' \item{FM}{character. Month of departure (start of trip). number with leading zeroes. 1=January, 12=December}
#' \item{FD}{character. Day of departure (start of trip). number with leading zeroes.}
#' \item{AH}{character. Port of departure (start of trip). Code identifying port.}
#' \item{LM}{character. Month of landing of catch. number with leading zeroes. 1=January, 12=December}
#' \item{LD}{character. Day of landing of catch. number with leading zeroes.}
#' \item{LH}{character. Port where catch was landed. Code identifying port.}
#' \item{RE}{character. Gear. Main gear for fishing operation definitions that allow several.}
#' \item{MA}{character. Mesh size (mm) for meshed gear.}
#' \item{HA}{character. Number of hauls/sets for fishing operation definitions that allow several.}
#' \item{VAR}{numeric. Total fishing time (hours).}
#' \item{OMRA}{character. International area code (ICES, NAFO, etc.)}
#' \item{OKSO}{character. Economic zone. Three letter code.}
#' \item{HO}{character. Main area of catch. As identified by the column 'StratumName' in \code{\link[RstoxFDA]{mainareaFdir2017}} or \code{\link[RstoxFDA]{mainareaFdir2018}})}
#' \item{LO}{character. Location of catch. As identified by the column 'Lokasjon' in \code{\link[RstoxFDA]{locationsFdir2017}} or \code{\link[RstoxFDA]{locationsFdir2018}}, together with Main area (the column HAVOMR)}
#' \item{LENG}{numeric. vessel length (m).}
#' \item{BTON}{character. Gross tonnage of vessel.}
#' \item{TENH}{character. Tonnage units of vessel.}
#' \item{HEST}{character. Engine effect of vessel (Hp)}
#' \item{FISK}{character. Code for the species landed. NS9400.}
#' \item{VEKT}{numeric Liveweight (Roundweight) in kg.}
#' }
#'
#'
#' @name LstLogbookData
#' @concept Data types
#'
NULL
#' Age group statistics (ReportFdaData)
#'
#' @description
#' Results from catch at age estimations. The results may be presented
#' decomposed on combinations of aggregation variables, such as gear, area, stock etc.
#'
#' ReportFdaData is a \code{\link[data.table]{data.table}} which may have the following columns:
#' \describe{
#' \item{AgeGroup}{character. The age group the estimate is reported for. May be age or plus group}
#' \item{Age}{integer. The lower age the estimate is reported for. May be an age or lower limit of plus group (inclusive)}
#' \item{LengthGroup}{character. The length group the estimate is reported for.}
#' \item{Length}{numeric. The upper length of the length group.}
#' \item{<Statistic>}{A reported statistic}
#' \item{SD}{Standard deviation for the reported statistic.}
#' \item{Low}{The lower limit of the estimated interval for the reported statistic.}
#' \item{High}{The higher limit of the estimated interval for the reported statistic.}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' @name ReportFdaData
#' @concept Data types
#'
NULL
#' Total catch statistics (ReportFdaSummaryData)
#'
#' @description
#' Results from catch estimations. The results may be presented
#' decomposed on combinations of aggregation variables, such as gear, area, stock etc.
#'
#' list with six members 'MeanAge', 'MeanWeight', 'MeanLength', 'TotalWeight',
#' 'TotalNumber', and 'GroupingVariables'.
#' 'MeanAge', 'MeanWeight', 'MeanLength', 'TotalWeight',
#' 'TotalNumber' are \code{\link[data.table]{data.table}}s with the columns:
#' \describe{
#' \item{<Statistic>}{The reported statistic, either 'MeanIndividualAge', 'MeanIndividualWeight', 'MeanIndividualLength', 'TotalWeight', or 'TotalNumber'}
#' \item{SD}{Standard deviation for the reported statistic.}
#' \item{Low}{The lower limit of the estimated interval for the reported statistic.}
#' \item{High}{The higher limit of the estimated interval for the reported statistic.}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaSummaryData
#' @concept Data types
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtAgeData)
#'
#' @description
#' A list with two members: 'NbyAge' and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CatchAtAge': the total catch at age in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaCatchAtAgeData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Covariance Report (ReportFdaCatchAtAgeCovarianceData)
#'
#' @description
#' Covariances from catch at age estimations. Covariances are presented
#' for age groups, together with any additional grouping (aggregation variables),
#' such as gear, area, stock etc.
#'
#' list with two members 'CovarianceNbyAge' and 'Variables'.
#' 'CovarianceNbyAge' is a \code{\link[data.table]{data.table}} which may have the following columns:
#' \describe{
#' \item{VariableId1}{Identifies the one of the variables the covariance is calculated for}
#' \item{VariableId2}{Identifies the other one of the variables the covariance is calculated for}
#' \item{Covariance}{The covariance of catch at age in numbers between groups identified by 'Variable1' and 'Variable2'.}
#' }
#' \describe{
#' \item{VariableId}{Identifier for variable that covariances are provided for.}
#' \item{AgeGroup}{character. The age group for the variable. May be age or plus group}
#' \item{Age}{integer. The lower age group for the variable. May be an age or lower limit of plus group (inclusive)}
#' \item{...}{Any aggregation variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#'
#' @name ReportFdaCatchAtAgeCovarianceData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtLengthData)
#'
#' @description
#' A list with two members: 'NbyLength', and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyLength}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CacthAtLength': the total catch at length in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#'
#' @name ReportFdaCatchAtLengthData
#' @concept Data types
#'
NULL
#' Fisheries dependent Catch At Age Report (ReportFdaCatchAtLengthAndAgeData)
#'
#' @description
#' A list with two members: 'NbyLengthAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{NbyLengthAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'CatchAtAgeLength': the total catch at length and age in numbers.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' @name ReportFdaCatchAtLengthAndAgeData
#' @concept Data types
#'
NULL
#' Fisheries dependent Length At Age Report (ReportFdaLengthAtAgeData)
#'
#' @description
#' A list with two members: 'MeanLengthByAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{MeanLengthByAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'MeanIndividualLength': the mean length.}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' Note that the summary statistics are reported for summaries of mean lengths,
#' so that e.g. SD report the standard deviation of the means,
#' and does not characterize the length distribution of fish.
#'
#' @name ReportFdaLengthAtAgeData
#' @concept Data types
#'
NULL
#' Reca Weight At Age Report (ReportFdaWeightAtAgeData)
#'
#' @description
#' A list with two members: 'MeanWeightByAge', and 'GroupingVariables'.
#'
#' \describe{
#' \item{MeanWeightByAge}{A \code{\link[RstoxFDA]{ReportFdaData}} table with reported <Statistic> being 'MeanIndividualWeight': the mean weight}
#' \item{GroupingVariables}{Any specified Grouping variables.}
#' }
#'
#' Units are configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' Note that the summary statistics are reported for summaries
#' of mean weights, so that e.g. SD report the standard deviation of the means,
#' and does not characterize the weight distribution of fish.
#'
#' @name ReportFdaWeightAtAgeData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ReportFdaData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaData}}
#' @param ReportFdaData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaData}}
#' @concept Data types
#' @export
is.ReportFdaData <- function(ReportFdaData){
if (!is.list(ReportFdaData)){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaData))){
return(FALSE)
}
if (length(ReportFdaData)<2){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaData[[1]])){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaData$GroupingVariables)){
return(FALSE)
}
if (!all(c("Age", "Low", "High", "SD") %in% names(ReportFdaData[[1]]))){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaData$GroupingVariables))){
return(FALSE)
}
return(TRUE)
}
#' Sum of Products report (ReportFdaSopData)
#'
#' @description
#' Sum of Products report (SOP-report), comparing the total landed weight of fish
#' with the product of mean weight at age estimates and total number
#' at age estimates
#'
#' list with two members 'SopReport' and 'GroupingVariables'.
#' 'SopReport' is a \code{\link[data.table]{data.table}} with the columns:
#' \describe{
#' \item{TotalWeightEstimated}{Total round weight estimated in kg}
#' \item{LandedWeight}{Landed round weight reported in kg}
#' \item{Difference}{The difference between estimated and reported landed weight in kg}
#' \item{RelativeDifference}{The difference between estimated and reported landed weight relative to reported weight}
#' \item{...}{Any aggregation variables. The names of these are listed in 'GroupingVariables'}
#' }
#' 'GroupingVariables' is a \code{\link[data.table]{data.table}} with a column containing the names of any aggregation variables.
#'
#' The unit for RelativeDifference is configurable, and can be inspected by \code{\link[RstoxData]{getUnit}}
#'
#' @name ReportFdaSopData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @param ReportFdaSOP argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSOP}}
#' @concept Data types
#' @export
is.ReportFdaSOP <- function(ReportFdaSOP){
if (!is.list(ReportFdaSOP)){
return(FALSE)
}
if (!all(c("GroupingVariables", "SopReport") %in% names(ReportFdaSOP))){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSOP$SopReport)){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSOP$GroupingVariables)){
return(FALSE)
}
if (!all(c("TotalWeightEstimated", "LandedWeight", "Difference", "RelativeDifference") %in% names(ReportFdaSOP$SopReport))){
return(FALSE)
}
if (!all(c("GroupingVariables") %in% names(ReportFdaSOP$GroupingVariables))){
return(FALSE)
}
return(TRUE)
}
#' Summary statistics for simulated parameters (ParameterizationSummaryData)
#'
#' @description
#' Summary statistics for (potentially multi-chained) simulated parameters,
#' such as MCMC simulations with Reca.
#' 'chains' in this respect refers to statistically independent simulations.
#'
#' list with two members 'ParameterSummary' and 'RunParameters',
#' which both all \code{\link[data.table]{data.table}}s. 'ParameterSummary'
#' contains parameter statistics for simulations and have the following columns:
#' \describe{
#' \item{Parameter}{Identifies the parameter that is summarized.}
#' \item{Mean}{Mean of the parameter}
#' \item{Variance}{Variance of the parameter}
#' \item{chainId}{Identifies the parameterization chain.}
#' }
#'
#' RunParameters summarizes global control parameters for each chain and has columns:
#' \describe{
#' \item{chainId}{Identifies the parameterization chain.}
#' \item{Iterations}{The number of iterations for parameter simulation}
#' }
#'
#' @seealso \code{\link[RstoxFDA]{RunRecaModels}} for running Reca-analysis
#' @seealso \code{\link[RstoxFDA]{ReportRecaParameterStatistics}}
#' for creating ParameterizationSummaryData from Reca-simulations.
#'
#' @name ParameterizationSummaryData
#' @concept Data types
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @param ParameterizationSummaryData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ParameterizationSummaryData}}
#' @concept Data types
#' @export
is.ParameterizationSummaryData <- function(ParameterizationSummaryData){
if (!is.list(ParameterizationSummaryData)){
return(FALSE)
}
if (!all(c("ParameterSummary", "RunParameters") %in% names(ParameterizationSummaryData))){
return(FALSE)
}
if (!data.table::is.data.table(ParameterizationSummaryData$ParameterSummary)){
return(FALSE)
}
if (!data.table::is.data.table(ParameterizationSummaryData$RunParameters)){
return(FALSE)
}
if (!all(c("chainId","Iterations") %in% names(ParameterizationSummaryData$RunParameters))){
return(FALSE)
}
if (!all(c("Parameter", "Mean", "Variance", "chainId") %in% names(ParameterizationSummaryData$ParameterSummary))){
return(FALSE)
}
return(TRUE)
}
#' Convergence Report for simulated parameters (ParameterConvergenceData)
#'
#' @description
#' Convergence Report for multi-chained simulated parameters,
#' such as MCMC simulations with Reca.
#' 'chains' in this respect refers to statistically independent simulations.
#'
#' list with one members 'ConvergenceReport',
#' which is a \code{\link[data.table]{data.table}} containing the following columns:
#' \describe{
#' \item{Parameter}{Identifies the parameter that is summarized.}
#' \item{InterVariance}{The Mean-Squared-Deviation the means of the parameter in each chain, to the mean across all chains}
#' \item{IntraVariance}{The mean of the within-chain variances of the parameter}
#' \item{GelmanRubinR}{Gelman-Rubins R}
#' }
#'
#' @details
#' Gelman-Rubins R is described by Gelman and Rubin (Statistical Science, 1992):
#' DOI: https://doi.org/10.1214/ss/1177011136
#'
#' @seealso \code{\link[RstoxFDA]{RunRecaModels}} for running Reca-analysis
#' @seealso \code{\link[RstoxFDA]{ReportParameterConvergence}}
#' for creating ParameterConvergenceData.
#'
#' @name ParameterConvergenceData
#' @concept Data types
#'
NULL
#' Checks if argument is \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @param ParameterConvergenceData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ParameterConvergenceData}}
#' @concept Data types
#' @export
is.ParameterConvergenceData <- function(ParameterConvergenceData){
if (!is.list(ParameterConvergenceData)){
return(FALSE)
}
if (!all(c("ConvergenceReport") %in% names(ParameterConvergenceData))){
return(FALSE)
}
cnames <- c("Parameter", "InterVariance", "IntraVariance", "GelmanRubinR")
if (!all(cnames %in% names(ParameterConvergenceData$ConvergenceReport))){
return(FALSE)
}
if (!data.table::is.data.table(ParameterConvergenceData$ConvergenceReport)){
return(FALSE)
}
return(TRUE)
}
#' Checks if argument is \code{\link[RstoxData]{Translation}}
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxData]{Translation}}
#' @param Translation argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxData]{Translation}}
#' @concept Data types
#' @export
is.Translation <- function(Translation){
if (!data.table::is.data.table(Translation)){
return(FALSE)
}
if (!all(c("NewValue") %in% names(Translation))){
return(FALSE)
}
if (ncol(Translation)<2){
return(FALSE)
}
return(TRUE)
}
#' Length Conversion Table (LengthConversionTable)
#'
#' @description
#' Length conversion parameters realting different length measurements.
#' @details
#' Length conversion factors relating different length measurements, such as 'standard length' and 'fork length'
#' based on a linear regression fit between length measurements:
#' L1 = alpha + beta \* L2,
#' where L1 and L2 are different length measurements
#' and 'alpha' and 'beta' are species-specific coefficients.
#'
#' \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{'Description'}{character: Free-text description of the product}
#' \item{'Species'}{character: Identifier for the species that the conversion applies to}
#' \item{'MeasurmentType'}{character: Identifier for the type of length measurement for the independent variable (L2 above)}
#' \item{'Alpha'}{numeric: scalar value representing the intercept (in cm) of a linear regression fit between length measurements.}
#' \item{'Beta'}{numeric: scalar value representing the slope of a linear regression fit between length measurements.}
#' }
#'
#' @name LengthConversionTable
#' @concept Data types
#'
NULL
#' Check if argument is LengthConversionTable
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{LengthConversionTable}}
#' @param LengthConversionTable argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{LengthConversionTable}}
#' @concept Data types
#' @export
is.LengthConversionTable <- function(LengthConversionTable){
if (!data.table::is.data.table(LengthConversionTable)){
return(FALSE)
}
if (!all(c("Description", "Species", "MeasurementType", "Alpha", "Beta") %in% names(LengthConversionTable))){
return(FALSE)
}
return(TRUE)
}
#' Weight Conversion Table (WeightConversionTable)
#'
#' @description
#' \code{\link[data.table]{data.table}} with factors for approximating the weight of a
#' desired product type (e.g. round fish)
#' from weights of other fish products. Contains the columns:
#' \describe{
#' \item{'Description'}{Free-text description of the product type}
#' \item{'Species'}{Identifier for the species that the conversion applies to}
#' \item{'ProductType'}{Identifier for the type of product that the conversion applies to}
#' \item{'WeightFactor'}{scalar value that weights for the given 'ProductType' can be multiplied with to approximate desired product type (e.g. round fish).}
#' }
#' NA is allowed for 'WeightFactor', which will result in NA for weights after conversion
#'
#' @name WeightConversionTable
#' @concept Data types
#'
NULL
#' Check if argument is WeightConversionTable
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{WeightConversionTable}}
#' @param WeightConversionTable argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{WeightConversionTable}}
#' @concept Data types
#' @export
is.WeightConversionTable <- function(WeightConversionTable){
if (!data.table::is.data.table(WeightConversionTable)){
return(FALSE)
}
if (!all(c("Description", "Species", "ProductType", "WeightFactor") %in% names(WeightConversionTable))){
return(FALSE)
}
return(TRUE)
}
#' Spatial landings overview data (PlotFisheriesOverviewSpatialData)
#'
#' @description
#' a ggplot object that renders catch density of landings on polygons.
#'
#' @name PlotFisheriesOverviewSpatialData
#' @concept Data types
#'
NULL
#' Temporal landings overview data (PlotFisheriesOverviewTemporalData)
#'
#' @description
#' a ggplot object that renders landings by date of catch and by grouping variables.
#'
#' @name PlotFisheriesOverviewTemporalData
#' @concept Data types
#'
NULL
#' Sampling Overview cell-plot data (PlotSamplingOverviewCellData)
#'
#' @description
#' a ggplot object that renders the a colored 'cell plot' overview of samples and landings.
#'
#' @name PlotSamplingOverviewCellData
#' @concept Data types
#'
NULL
#' Sampling variables plot data (PlotSamplingCoverageData)
#'
#' @description
#' a ggplot object that renders a a barplot of landings with color codes for how much each part of the fishery is sampled.
#'
#' @name PlotSamplingCoverageData
#' @concept Data types
#'
NULL
#' Sampling variables plot data (PlotSamplingVariablesData)
#'
#' @description
#' a ggplot object that renders a stacked barplot of sampling variables for each part of the fishery, with total landings on a secondary axis.
#'
#' @name PlotSamplingVariablesData
#' @concept Data types
#'
NULL
#' Trace plot (PlotPosteriorTracesData)
#'
#' @description
#' a ggplot object that renders traceplot of Reca predictions.
#'
#' @name PlotPosteriorTracesData
#' @concept Data types
#'
NULL
#' Total catch by age plot (PlotCatchAtAgeTotalsData)
#'
#' @description
#' a ggplot object that renders estimated total catch by age.
#'
#' @name PlotCatchAtAgeTotalsData
#' @concept Data types
#'
NULL
#' Mean weight at age plot (PlotMeanWeightAtAgeData)
#'
#' @description
#' a ggplot object that renders estimated mean weight by age.
#'
#' @name PlotMeanWeightAtAgeData
#' @concept Data types
#'
NULL
#' Mean length at age plot (PlotMeanLengthAtAgeData)
#'
#' @description
#' a ggplot object that renders estimated mean length by age.
#'
#' @name PlotMeanLengthAtAgeData
#' @concept Data types
#'
NULL
#' Sampling Report data (ReportFdaSamplingData)
#'
#' @description
#' list with tow members:
#' \describe{
#' \item{GroupingVariables}{a \code{\link[data.table]{data.table}} with the variables used for aggregation in 'FisheriesSampling' stored in the column 'GroupingVariables'}
#' \item{SamplingVariables}{a \code{\link[data.table]{data.table}} with the variables used for partitioning samples in 'FisheriesSampling' stored in the column 'SamplingVariables'}
#' \item{FisheriesSampling}{a \code{\link[data.table]{data.table}} described below.}
#' }
#'
#' FisheriesSampling is a report of sampling against total landings for partitions of a fishery.
#' The report is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{...}{A column for each of the provided Aggregation variables}
#' \item{...}{A column for each of the provided Sampling variables}
#' \item{LandedRoundWeight}{Total landings in kg}
#' \item{Catches}{Number of unique catches sampled}
#' \item{Vessels}{Number of unique vessels sampled}
#' \item{WeightMeasurements}{Number of fished measured for weight}
#' \item{LengthMeasurements}{Number of fished measured for length}
#' \item{AgeReadings}{Number of fished with age determined}
#' \item{WeightOfSampledCatches}{Total weight of the sampled catches}
#' }
#'
#' @name ReportFdaSamplingData
#' @concept Data types
#'
NULL
#' Check if argument is ReportFdaSamplingData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSamplingData}}
#' @param ReportFdaSamplingData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{ReportFdaSamplingData}}
#' @concept Data types
#' @export
is.ReportFdaSamplingData <- function(ReportFdaSamplingData){
if (!is.list(ReportFdaSamplingData)){
return(FALSE)
}
if (!all(c("GroupingVariables", "SamplingVariables", "FisheriesSampling") %in% names(ReportFdaSamplingData))){
return(FALSE)
}
if (!data.table::is.data.table(ReportFdaSamplingData$FisheriesSampling)){
return(FALSE)
}
if (!all(c("LandedRoundWeight", "Catches", "Vessels", "WeightMeasurements", "LengthMeasurements", "AgeReadings", "WeightOfSampledCatches") %in% names(ReportFdaSamplingData$FisheriesSampling))){
return(FALSE)
}
return(TRUE)
}
#' Landings Report data (ReportFdaLandingData)
#'
#' @description
#' list with tow members:
#' \describe{
#' \item{GroupingVariables}{a \code{\link[data.table]{data.table}} with the variables used for aggregation in 'FisheriesLandings' stored in the column 'GroupingVariables'}
#' \item{FisheriesLandings}{a \code{\link[data.table]{data.table}} described below.}
#' }
#'
#' FisheriesLandings is a report of landings for partitions of a fishery.
#' The report is a \code{\link[data.table]{data.table}} with columns:
#' \describe{
#' \item{...}{A column for each of the provided Aggregation variables}
#' \item{LandedRoundWeight}{Total landings in kg}
#' }
#'
#' @name ReportFdaLandingData
#' @concept Data types
#'
NULL
#' Reca Data (RecaData)
#'
#' Data and some data parameters prepared for running
#' \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}
#' via \code{\link[RstoxFDA]{RunRecaEstimate}}.
#'
#' A list with five members:
#'
#' @details
#' \describe{
#' \item{AgeLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{WeightLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{Landings}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. List of \code{\link[data.table]{data.table}}}
#' \item{GlobalParameters}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. see details. List of \code{\link[data.table]{data.table}}}
#' \item{CovariateMaps}{Mapping of values for each covariate in landings and samples (including non-configurable catchId) to integer value used in R-ECA. List of \code{\link[data.table]{data.table}}}
#' }
#'
#' @name RecaData
#' @concept Data types
#'
NULL
#' Check if argument is RecaData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaData}}
#' @param RecaData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaData}}
#' @concept Data types
#' @export
is.RecaData <- function(RecaData){
if (!is.list(RecaData)){
return(FALSE)
}
if (!all(c("AgeLength", "WeightLength", "Landings", "GlobalParameters") %in% names(RecaData))){
return(FALSE)
}
if (!is.list(RecaData$AgeLength)){
return(FALSE)
}
if (!is.list(RecaData$WeightLength)){
return(FALSE)
}
if (!is.list(RecaData$Landings)){
return(FALSE)
}
if (!is.list(RecaData$GlobalParameters)){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaData$AgeLength))){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaData$WeightLength))){
return(FALSE)
}
if (!all(c("AgeLengthCov", "WeightLengthCov", "LiveWeightKG") %in% names(RecaData$Landings))){
return(FALSE)
}
return(TRUE)
}
#' Reca Parameter Data (RecaParameterData)
#'
#' @description
#' Data and some data parameters prepared for running
#' various report functions that invoke \code{\link[Reca]{eca.predict}}.
#'
#' @section model fit:
#' For inspection or analysis of model fit, the lists 'FitProportionAtAge',
#' 'FitLengthGivenAge' and 'FitWeightGivenLength' is of interest. For stock-splitting analysis,
#' the lists FitLengthGivenAgeCC and FitWeightGivenLengthCC will be added as well, corresponding to one of the stocks.
#' These lists correspond to the three Reca-models and contain:
#' \describe{
#' \item{LogLikeliehood}{A \code{\link[data.table]{data.table}}
#' tabulating the logarithm of the likeliehood of the parameter set for each iteration}
#' \item{...}{A \code{\link[data.table]{data.table}} for each of the model effects (e.g. covariates).}
#' }
#'
#' In addition to configurable covariates, the models always contain a constant effect (named 'constant'),
#' a catch or haul effect (named 'catchSample') and effects for fish measurements (named 'fish').
#' Where relevant the following parameters may be tabulated for each effect:
#' \describe{
#' \item{Age}{Identifying the age the effect applies to}
#' \item{Level}{Identifying the value or level of the covariate the effect applies to}
#' \item{Iteration}{Identifying the iteration the fit is provided for}
#' \item{AgeIndex}{Age identifier used internally in Reca}
#' \item{LevelIndex}{Level identifier used internally in Reca}
#' \item{Slope}{The value of the regression slope}
#' \item{tau_Slope}{The value of tau parameter for the regression slope}
#' \item{ar_Slope}{The value of regression slope of a the autoregressive coefficient associated with the effect}
#' \item{Intercept}{The value of the regression intercept}
#' \item{tau_Intercept}{The value of tau parameter for the regression intercept}
#' \item{ar_Intercept}{The value of the regression intercept of a autoregressive coefficient associated with the effect}
#' }
#' Consult Hirst et.al. 2005 for description of the parameters
#'
#' @section other data:
#' The lists 'AgeLength', 'WeightLength', 'Landings', 'GlobalParameters' and 'CovariateMaps'
#' may be passed to \code{\link[Reca]{eca.predict}} in functions consuming output from this function. All in all
#' the following lists can be accessed on RecaParameterData objects:
#' \describe{
#' \item{FitProportionAtAge}{list of data tables with parameters for for the Proportion-at-age model}
#' \item{FitLengthGivenAge}{list of data tables with parameters for for the Length-given-age model}
#' \item{FitWeightGivenLength}{list of data tables with parameters for for the Weight-given-length model}
#' \item{AgeLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{WeightLength}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{Landings}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{GlobalParameters}{input needed for \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}. see details}
#' \item{CovariateMaps}{Mapping of values for each covariate in landings and samples (including non-configurable catchId) to integer value used in R-ECA.}
#' }
#'
#' @name RecaParameterData
#' @concept Data types
#'
NULL
#' Check if argument is RecaParameterData
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaParameterData}}
#' @param RecaParameterData argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaParameterData}}
#' @concept Data types
#' @export
is.RecaParameterData <- function(RecaParameterData){
if (!is.list(RecaParameterData)){
return(FALSE)
}
if (!all(c("FitProportionAtAge", "FitLengthGivenAge", "FitWeightGivenLength", "AgeLength", "WeightLength", "Landings", "GlobalParameters") %in% names(RecaParameterData))){
return(FALSE)
}
if (!is.list(RecaParameterData$AgeLength)){
return(FALSE)
}
if (!is.list(RecaParameterData$WeightLength)){
return(FALSE)
}
if (!is.list(RecaParameterData$Landings)){
return(FALSE)
}
if (!is.list(RecaParameterData$GlobalParameters)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitProportionAtAge)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitLengthGivenAge)){
return(FALSE)
}
if (!is.list(RecaParameterData$FitWeightGivenLength)){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaParameterData$AgeLength))){
return(FALSE)
}
if (!all(c("DataMatrix", "CovariateMatrix", "info") %in% names(RecaParameterData$WeightLength))){
return(FALSE)
}
if (!all(c("AgeLengthCov", "WeightLengthCov", "LiveWeightKG") %in% names(RecaParameterData$Landings))){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitProportionAtAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitProportionAtAge$LogLikelihood)){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitLengthGivenAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitLengthGivenAge$LogLikelihood)){
return(FALSE)
}
if (!all(c("LogLikelihood") %in% names(RecaParameterData$FitWeightGivenLength))){
return(FALSE)
}
if (!data.table::is.data.table(RecaParameterData$FitWeightGivenLength$LogLikelihood)){
return(FALSE)
}
return(TRUE)
}
#' Reca Results (RecaCatchAtAge)
#'
#' @description
#' Posterior distribution of total catch at age and weight and length parameters.
#'
#' @details
#' a list of data tables:
#' \describe{
#' \item{CatchAtAge}{\code{\link[data.table]{data.table}} tabulating the estimated catch-at-age by length group for each Reca iteration (MCMC sample)}
#' \item{MeanLength}{\code{\link[data.table]{data.table}} tabulating the mean length in cm by age for each Reca iteration (MCMC sample)}
#' \item{MeanWeight}{\code{\link[data.table]{data.table}} tabulating the mean weight in g by age for each Reca iteration (MCMC sample)}
#' \item{GroupingVariables}{\code{\link[data.table]{data.table}} with any variables that catch-at-age estimates are partitioned on
#' in the column 'GroupingVariables'. These may correspond to variables in the landings, or maye be the variable 'Stock' if
#' stock-splitting analysis have been perfomred.}
#' }
#' In addition to columns for the variables in 'GroupingVariables', the data tables 'CatchAtAge', 'MeanLength', and 'MeanWeight' have the following variables:
#' \describe{
#' \item{Age}{Age in number of years.}
#' \item{Iteration}{The Reca iteration (MCMC sample) that estimates are calculated for}
#' }
#'
#' 'CatchAtAge' also have the variables
#' \describe{
#' \item{Length}{Upper limit of length group in cm}
#' \item{CatchAtAge}{The total catch at age in numbers}
#' }
#'
#' 'MeanLength' has the variable:
#' \describe{
#' \item{MeanIndividualLength}{Mean Length at age in cm}
#' }
#'
#' 'MeanWeight' has the variable:
#' \describe{
#' \item{MeanIndividualWeight}{Mean weight at age in g}
#' }
#'
#' @name RecaCatchAtAge
#' @concept Data types
#'
NULL
#' Check if argument is RecaCatchAtAge
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaCatchAtAge}}
#' @param RecaCatchAtAge argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaCatchAtAge}}
#' @concept Data types
#' @export
is.RecaCatchAtAge <- function(RecaCatchAtAge){
if (!is.list(RecaCatchAtAge)){
return(FALSE)
}
if (!all(c("CatchAtAge", "MeanLength", "MeanWeight") %in% names(RecaCatchAtAge))){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$CatchAtAge)){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$MeanLength)){
return(FALSE)
}
if (!data.table::is.data.table(RecaCatchAtAge$MeanWeight)){
return(FALSE)
}
if (!all(c("Length", "Age", "Iteration", "CatchAtAge") %in% names(RecaCatchAtAge$CatchAtAge))){
return(FALSE)
}
if (!all(c("MeanIndividualLength", "Age", "Iteration") %in% names(RecaCatchAtAge$MeanLength))){
return(FALSE)
}
if (!all(c("MeanIndividualWeight", "Age", "Iteration") %in% names(RecaCatchAtAge$MeanWeight))){
return(FALSE)
}
return(TRUE)
}
#' Reca Results (RecaResult)
#'
#' Results from running
#' \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}
#' via \code{\link[RstoxFDA]{RunRecaEstimate}}.
#'
#' @details
#'
#' \describe{
#' \item{input}{All input data and parameters provided to \code{\link[Reca]{eca.estimate}} and \code{\link[Reca]{eca.predict}}}
#' \item{fit}{as returned by \code{\link[Reca]{eca.estimate}}}
#' \item{prediction}{as returned by \code{\link[Reca]{eca.predict}}}
#' \item{covariateMaps}{list() mapping from Reca covariate encoding to values fed to \code{\link[RstoxFDA]{PrepareRecaEstimate}}. As in \code{\link[RstoxFDA]{RecaData}}}
#' }
#'
#' @name RecaResult
#' @concept Data types
#'
NULL
#' @noRd
is.RecaPrediction <- function(prediction){
if (!is.list(prediction)){
return(FALSE)
}
if (!all(c("TotalCount", "MeanLength", "MeanWeight", "AgeCategories", "LengthIntervalsLog") %in% names(prediction))){
return(FALSE)
}
if (!is.array(prediction$TotalCount)){
return(FALSE)
}
if (!is.array(prediction$MeanLength)){
return(FALSE)
}
if (!is.array(prediction$MeanWeight)){
return(FALSE)
}
return(TRUE)
}
#' Check if argument is RecaResult
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{RecaResult}}
#' @param RecaResult argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{RecaResult}}
#' @concept Data types
#' @export
is.RecaResult <- function(RecaResult){
if (!is.list(RecaResult)){
return(FALSE)
}
if (!all(c("input", "fit", "prediction", "covariateMaps") %in% names(RecaResult))){
return(FALSE)
}
if (!is.RecaData(RecaResult$input)){
return(FALSE)
}
if (!is.RecaPrediction(RecaResult$prediction)){
return(FALSE)
}
return(TRUE)
}
#' Temporal Categories (TemporalDefinition)
#'
#' Table (\code{\link[data.table]{data.table}}) defining a categorical variable for grouping data based on date.
#'
#' @details
#' \describe{
#' \item{TemporalCategory}{character() Value of the temporal category}
#' \item{StartDay}{integer() Day of month for first day in the temporal category (1-based)}
#' \item{StartMonth}{integer() Month for first day in the temporal category (1-based)}
#' \item{StartYear}{optional integer() Year for which the category is defined, omit this column for seasonal definitions.}
#' }
#'
#' Start and end of year is not implied as category delimitations when not included.
#' If 1st of January is not defined as the start of a category,
#' it is taken to be included in the last category of the preceding year.
#'
#' @name TemporalDefinition
#' @concept Data types
#'
NULL
#' Check if argument is TemporalDefinition
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{TemporalDefinition}}
#' @param TemporalDefinition argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{TemporalDefinition}}
#' @concept Data types
#' @export
is.TemporalDefinition <- function(TemporalDefinition){
if (!data.table::is.data.table(TemporalDefinition)){
return(FALSE)
}
#StartYear is optional.
if (!all(c("Period", "StartDay", "StartMonth") %in% names(TemporalDefinition))){
return(FALSE)
}
if (!all(names(TemporalDefinition) %in% c("Period", "StartDay", "StartMonth", "StartYear"))){
return(FALSE)
}
return(TRUE)
}
#' Area Code Positions (AreaPosition)
#'
#' Table (\code{\link[data.table]{data.table}}) defining a position for area codes.
#'
#' @details
#' \describe{
#' \item{Area}{Area code. (key)}
#' \item{Location}{optional subdivision of 'Area'}
#' \item{Latitude}{WGS84 Latitude, decimal degrees}
#' \item{Longitude}{WGS84 Longitude, decimal degrees}
#' }
#' If location is provided, the case for missing location is also encoded.
#'
#' @name AreaPosition
#' @concept Data types
#'
NULL
#' Check if argument is AreaPosition
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{AreaPosition}}
#' @param AreaPosition argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{AreaPosition}}
#' @concept Data types
#' @export
is.AreaPosition <- function(AreaPosition){
if (!data.table::is.data.table(AreaPosition)){
return(FALSE)
}
if (!all(c("Area", "Location", "Latitude", "Longitude") %in% names(AreaPosition))){
return(FALSE)
}
return(TRUE)
}
#' Area Neighbour Definition (CarNeighbours)
#'
#' Table (\code{\link[data.table]{data.table}})
#' defining neighbours for a CAR-variable (Conditional autoregressive variable).
#'
#' @details
#' \describe{
#' \item{CarValue}{Values for a variable used as CAR-variable}
#' \item{Neighbours}{Comma-separated list of neighbours}
#' }
#'
#' The table is symmetric, so that if b is a neighbour of a. a is also a neighbour of b.
#'
#' @name CarNeighbours
#' @concept Data types
#'
NULL
#' Landing data (LandingData)
#'
#' @description
#' See \code{\link[RstoxData]{LandingData}}
#'
#' @name LandingData
#' @concept Data types
#'
NULL
#' Check if argument is CarNeighbours
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{CarNeighbours}}
#' @param CarNeighbours argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{CarNeighbours}}
#' @concept Data types
#' @export
is.CarNeighbours <- function(CarNeighbours){
if (!data.table::is.data.table(CarNeighbours)){
return(FALSE)
}
if (!all(c("CarValue", "Neighbours") %in% names(CarNeighbours))){
return(FALSE)
}
return(TRUE)
}
#' Age Error Matrix (AgeErrorMatrix)
#'
#' Table (\code{\link[data.table]{data.table}})
#' defining probabilities of misreading age.
#'
#' @details
#' \describe{
#' \item{columns 1..n}{numeric() [0,1]. Probability of reading read age, given that true age is as column name.}
#' \item{ReadAge}{The read age.}
#' }
#'
#' Columns sum to 1.
#'
#' @name AgeErrorMatrix
#' @concept Data types
#'
NULL
#' Check if argument is AgeErrorMatrix
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{AgeErrorMatrix}}
#' @param AgeErrorMatrix argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{AgeErrorMatrix}}
#' @concept Data types
#' @export
is.AgeErrorMatrix <- function(AgeErrorMatrix){
if (!data.table::is.data.table(AgeErrorMatrix)){
return(FALSE)
}
if (!("ReadAge" %in% names(AgeErrorMatrix))){
return(FALSE)
}
return(TRUE)
}
#' Stock splitting parameters (StockSplittingParameters)
#'
#' @description
#' Table (\code{\link[data.table]{data.table}})
#'
#' Defining parameters for the stock-splitting analysis in Reca, including
#' parameters for the probability of misclassifying when determining stock membership of a specimen.
#'
#' The stock splitting analysis allows catch at age to be estimated for two domains that partition all individuals,
#' and that are observed for all age-determined specimens. It was developed for disciminating Coastal Cod and North East Arctic Cod,
#' based on otholith growth patterns, and naming conventions are derived from that. It could be adapted to
#' other stocks and in principle to other bipartite domain definitions (such as Sex).
#'
#' Two otolith patterns are defined for each of the two stocks 'CC' and 'S'. Otolith type 1 and 2 identifies
#' that a specimen belongs to the stock 'CC', and are interpreted by otoloith readers as 'certain' and 'uncertain' CC, respectively.
#' Otolith type 4 and 5 identifies that a specimen belongs to the stock 'S', and are interpreted as 'uncertain' and 'certain' S, respectively.
#'
#' \describe{
#' \item{StockNameCC}{Name of the stock identified as CC}
#' \item{StockNameS}{Name of the stock identified as S}
#' \item{ProbabilityType1As1}{Probability of classifying a type 1 specimen as type 1 (certain CC).}
#' \item{ProbabilityType5As1}{Probability of classifying a type 5 (certain S) specimen as type 1 (certain CC).}
#' \item{ProbabilityType2As2}{Probability of classifying a type 2 (uncertain CC) specimen as type 2 (uncertain CC).}
#' \item{ProbabilityType4As2}{Probability of classifying a type 4 (uncertain S) specimen as type 2 (uncertain CC).}
#' \item{ProbabilityType2As4}{Probability of classifying a type 2 (uncertain CC) specimen as type 4 (uncertain S).}
#' \item{ProbabilityType4As4}{Probability of classifying a type 4 (uncertain S) specimen as type 4 (uncertain S).}
#' \item{ProbabilityType1As5}{Probability of classifying a type 1 (certain CC) specimen as type 5 (certain S).}
#' \item{ProbabilityType5As5}{Probability of classifying a type 5 (certain S) specimen as type 5 (certain S).}
#' }
#'
#' The probabilities for different ways to classify a type must sum to 1.
#' E.g.: ProbabilityType1As1 + ProbabilityType1As5 = 1.
#'
#' The data table contains only one row.
#'
#' @name StockSplittingParameters
#' @concept Data types
#'
NULL
#' Check if argument is StockSplittingParameters
#' @description
#' Checks if argument conforms to specification for \code{\link[RstoxFDA]{StockSplittingParameters}}
#' @param StockSplittingParameters argument to be checked for data conformity
#' @return logical, TRUE if argument conforms to specification for \code{\link[RstoxFDA]{StockSplittingParameters}}
#' @concept Data types
#' @export
is.StockSplittingParameters <- function(StockSplittingParameters){
if (!data.table::is.data.table(StockSplittingParameters)){
return(FALSE)
}
if (!all(c("StockNameCC", "StockNameS", "ProbabilityType1As1",
"ProbabilityType1As5", "ProbabilityType2As2",
"ProbabilityType2As4", "ProbabilityType4As2",
"ProbabilityType4As4", "ProbabilityType5As1",
"ProbabilityType5As5") %in% names(StockSplittingParameters))){
return(FALSE)
}
if (nrow(StockSplittingParameters) != 1){
return(FALSE)
}
prob <- function(arg){
if (arg<0 | arg > 1){
return(FALSE)
}
return(TRUE)
}
if (!prob(StockSplittingParameters$ProbabilityType1As1)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType1As5)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType2As2)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType2As4)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType4As2)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType4As4)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType5As1)){
return(FALSE)
}
if (!prob(StockSplittingParameters$ProbabilityType5As5)){
return(FALSE)
}
return(TRUE)
}
plotDefaultsCellPlotColors <- list(
MinVessels=2,
MinCatches=2,
MinMeasurements=100,
ColorNoSamples = "#ffffcc",
ColorFewMeasurements = "#c2e699",
ColorFewCatches = "#78c679",
ColorFewVessels = "#31a354",
ColorGoodSampling = "#006837"
)
#' Function specification for inclusion in StoX UI
#' @export
stoxFunctionAttributes <- list(
ReadLandingFDA = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "LandingData",
functionParameterFormat = list(FileNames = "filePaths"),
functionArgumentHierarchy = list()
),
DefineStockSplittingParameters = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "StockSplittingParameters",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
),
StockNameCC=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
StockNameS=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType1As1=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType5As1=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType2As2=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType4As2=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType2As4=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType4As4=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType1As5=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
),
ProbabilityType5As5=list(
DefinitionMethod = "FunctionParameters",
UseProcessData = FALSE
)
)
),
DefineCarNeighbours = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "CarNeighbours",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
StratumPolygon = list(
DefinitionMethod = "StratumPolygon",
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefineAgeErrorMatrix = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "AgeErrorMatrix",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
)
)
),
DefineAreaPosition = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "AreaPosition",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
StratumPolygon = list(
DefinitionMethod = "StratumPolygon",
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefinePeriod = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "TemporalDefinition",
functionParameterFormat = list(
CustomPeriods = "periodvector"
),
functionArgumentHierarchy = list(
TemporalCategory = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
CustomPeriods = list(
TemporalCategory = "Custom",
UseProcessData = FALSE
)
)
),
DefineWeightConversionFactor = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "WeightConversionTable",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
DefineLengthConversionParameters = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "LengthConversionTable",
functionParameterFormat = list(
FileName = "filePath"
),
functionArgumentHierarchy = list(
DefinitionMethod = list(
UseProcessData = FALSE
),
# These two are joined with AND, and must both be fulfilled:
FileName = list(
DefinitionMethod = "ResourceFile",
UseProcessData = FALSE
)
)
),
LoadFdaStratumPolygon = list(
functionType = "processData",
functionCategory = "baseline",
functionOutputDataType = "StratumPolygon",
functionArgumentHierarchy = list(
StrataSystem = list(
UseProcessData = FALSE
)
)
),
ConvertWeightBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
ConvertLengthBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetTimeBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetShortGearBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetStartDateBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
SetAreaPositionsBiotic =list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
AddAreaPositionStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData"
),
AddGearGroupStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData"
),
AddGearGroupStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
AddStratumStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData",
functionParameterDefaults = list(
ColumnName = "Stratum"
)
),
AddStratumStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
ColumnName = "Stratum"
)
),
AddPeriodStoxLanding = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxLandingData",
functionParameterDefaults = list(
ColumnName = "Period"
)
),
AddPeriodStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
ColumnName = "Period"
)
),
ComputeIndividualSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "IndividualSamplingParametersData",
functionParameterFormat = list(
Parameters = "individualparameters",
StratificationColumns = "individualstratificationcolumns"
),
functionArgumentHierarchy = list(
StratificationColumns = list(
DefinitionMethod = "Stratified"
),
LengthInterval = list(
DefinitionMethod = "LengthStratified"
)
)
),
CollapseStrata = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "IndividualSamplingParametersData",
functionParameterFormat = list(
RetainStrata = "collapsestrataretain"
)
),
ComputePSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
SamplingUnitId = "samplingunitid",
StratificationColumns = "stratificationcolumns"
),
functionParameterDefaults = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
functionArgumentHierarchy = list(
StratificationColumns = list(
DefinitionMethod = "AdHocStoxBiotic"
),
StratumName = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
Quota = list(
DefinitionMethod = "ProportionalPoissonSampling"
),
ExpectedSampleSize = list(
DefinitionMethod = "ProportionalPoissonSampling"
)
)
),
ReadPSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
FileName = "filePath"
)
),
AddPsuStratificationVariables = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
StratificationVariables = "stratificationvariablesvector",
StratificationVariablesTable = "stratificationvariablestable"
)
),
AssignPSUSamplingParameters = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "PSUSamplingParametersData",
functionParameterFormat = list(
SamplingUnitId = "samplingunitid",
DataRecordId = "datarecordid"
),
functionParameterDefaults = list(
DefinitionMethod = "MissingAtRandom"
)
),
AnalyticalPSUEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPSUEstimateData",
functionParameterFormat = list(
Variables = "individualnumericvariables",
DomainVariables = "individualdomainvariables",
PSUDomainVariables = "psudomainvariables"
)
),
AnalyticalPopulationEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData"
),
AnalyticalRatioEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
WeightVariable = "IndividualRoundWeight"
),
functionParameterFormat = list(
WeightVariable = "weightvariableratioestimate",
StratificationVariables = "stratificationvariableslandings",
DomainVariables = "domainvariableslandings"
)
),
ExtendAnalyticalSamplingFrameCoverage = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
Method = "SetToStratum",
UnsampledStratum = "Out-of-frame"
),
functionParameterFormat = list(
StratificationVariables = "stratificationvariableslandings",
SourceStratum = "sourcestratum"
),
functionArgumentHierarchy = list(
SourceStratum = list(
Method = "SetToStratum"
)
)
),
InterpolateAnalyticalDomainEstimates = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
Method = "StratumMean",
Epsilon = 1e-4
),
functionParameterFormat = list(
DomainMarginVariables = "domainvariableslandings"
),
functionArgumentHierarchy = list(
Epsilon = list(
Method = "StratumMean"
)
)
),
AggregateAnalyticalEstimate = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "AnalyticalPopulationEstimateData",
functionParameterDefaults = list(
RetainStrata=character(),
AggregateStratumName="Aggregate"
),
functionParameterFormat = list(
RetainStrata = "retainstrata"
)
),
ListBioticDifference = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "BioticData"
),
FilterAgeLengthOutliersStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
FilterWeightLengthOutliersStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData"
),
PrepareRecaEstimate = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaData",
functionParameterFormat = list(
RandomEffects = "randomcovariates",
CarEffect = "carcovariate",
FixedEffects = "fixedcovariates"),
functionArgumentHierarchy = list(
AgeErrorMatrix = list(
UseAgingError = TRUE
),
CarNeighbours = list(
UseCarEffect = TRUE
),
CarEffect = list(
UseCarEffect = TRUE
),
StockSplittingParameters = list(
UseStockSplitting = TRUE
),
UseStockSplittingError = list(
UseStockSplitting = TRUE
)
),
functionParameterDefaults = list(
CellEffect = "Off",
HatchDay = 1
)
),
ParameterizeRecaModels = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaParameterData",
functionParameterDefaults = list(
Lgamodel = "log-linear",
Delta.age = 0.001,
Thin = 0
)
#doesnt work for directory ?
#functionParameterFormat = list(
# ResultDirectory = "filePath"
#)
),
RunRecaModels = list(
functionType = "modelData",
functionCategory = "analysis",
functionOutputDataType = "RecaCatchAtAge",
functionParameterFormat = list(
GroupingVariables = "GroupingVariables"
),
functionParameterDefaults = list(
TemporalResolution = "Quarter",
Caa.burnin = 0
)
),
ReportFdaSampling = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaSamplingData",
functionParameterFormat = list(
GroupingVariables = "samplereportvariables",
SamplingVariables = "onlysamplereportvariables"
),
functionParameterDefaults = list(
Decimals = 0,
Unit = "kg"
)
),
ReportFdaLandings = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLandingData",
functionParameterFormat = list(
GroupingVariables = "landingsreportvariables"
),
functionParameterDefaults = list(
Decimals = 0,
Unit = "kg"
)
),
ReportRecaCatchAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportAnalyticalCatchAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "individuals"
)
),
AddLengthGroupStoxBiotic = list(
functionType = "modelData",
functionCategory = "baseline",
functionOutputDataType = "StoxBioticData",
functionParameterDefaults = list(
LengthGroupVariable = "LengthGroup",
LeftOpen = TRUE
)
),
ReportAnalyticalCatchAtLength = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "individuals"
),
functionParameterFormat = list(
LengthGroupVariable = "domainvariablecatchatlength"
)
),
ReportAnalyticalWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaWeightAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "g"
)
),
ReportAnalyticalLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLengthAtAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9,
Unit = "cm"
)
),
ReportRecaCatchAtAgeCovariance = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtAgeCovarianceData",
functionParameterDefaults = list(
Decimals = 0
)
),
ReportRecaCatchAtLength = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportRecaCatchAtLengthAndAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchAtLengthAndAgeData",
functionParameterDefaults = list(
Decimals = 0,
IntervalWidth = 0.9
)
),
ReportRecaLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaLengthAtAgeData",
functionParameterDefaults = list(
Decimals = 2,
IntervalWidth = 0.9,
Threshold=0
)
),
ReportRecaWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaWeightAtAgeData",
functionParameterDefaults = list(
Decimals = 2,
IntervalWidth = 0.9,
Threshold=0
)
),
ReportRecaCatchStatistics = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaCatchSummaryData",
functionArgumentHierarchy = list(
DecimalTotalNumber = list(
UseDefaultDecimalOptions = FALSE
),
DecimalTotalWeight = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanAge = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanWeight = list(
UseDefaultDecimalOptions = FALSE
),
DecimalMeanLength = list(
UseDefaultDecimalOptions = FALSE
),
UnitTotalNumber = list(
UseDefaultUnitOptions = FALSE
),
UnitTotalWeight = list(
UseDefaultUnitOptions = FALSE
),
UnitMeanWeight = list(
UseDefaultUnitOptions = FALSE
),
UnitMeanLength = list(
UseDefaultUnitOptions = FALSE
)
),
functionParameterDefaults = list(
DecimalTotalNumber = 0,
DecimalTotalWeight = 0,
DecimalMeanAge = 1,
DecimalMeanWeight = 3,
DecimalMeanLength = 3,
UnitTotalNumber = "10^6 individuals",
UnitTotalWeight = "kiloton",
UnitMeanWeight = "kg",
UnitMeanLength = "cm",
IntervalWidth = 0.9
)
),
ReportFdaSOP = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ReportFdaSopData",
functionParameterFormat = list(
GroupingVariables = "GroupingVariablesSop"
),
functionParameterDefaults = list(
DecimalWeight = 0,
DecimalFraction = 3
)
),
ReportRecaParameterStatistics = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ParameterizationSummaryData",
functionArgumentHierarchy = list(
ParameterizationSummaryData = list(
AppendReport = TRUE
)
)
),
ReportParameterConvergence = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "ParameterConvergenceData",
functionParameterDefaults = list(
Tolerance = 0.1,
Decimals = 2
)
),
PlotFisheriesOverviewSpatial = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotFisheriesOverviewSpatialData"
),
PlotFisheriesOverviewTemporal = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotFisheriesOverviewTemporalData"
),
PlotSamplingOverviewCell = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingOverviewCellData",
functionParameterFormat = list(
ColumnVariable = "columnvariablecellplot"
),
functionArgumentHierarchy = list(
MinVessels = list(
UseDefaultColorSettings = FALSE
),
MinCatches = list(
UseDefaultColorSettings = FALSE
),
MinMeasurements = list(
UseDefaultColorSettings = FALSE
),
ColorNoSamples = list(
UseDefaultColorSettings = FALSE
),
ColorFewMeasurements = list(
UseDefaultColorSettings = FALSE
),
ColorFewCatches = list(
UseDefaultColorSettings = FALSE
),
ColorFewVessels = list(
UseDefaultColorSettings = FALSE
),
ColorGoodSampling = list(
UseDefaultColorSettings = FALSE
)
),
functionParameterDefaults = c(
plotDefaultsCellPlotColors,
list(Measurement="AgeReadings")
)
),
PlotSamplingCoverage = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingCoverageData",
functionArgumentHierarchy = list(
Measurement = list(
ColorScheme = "CellPlot"
),
MinVessels = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
MinCatches = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
MinMeasurements = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorNoSamples = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewMeasurements = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewCatches = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorFewVessels = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
ColorGoodSampling = list(
UseDefaultColorSettings = FALSE,
ColorScheme = "CellPlot"
),
SamplingUnit = list(
ColorScheme = "Gradient"
),
GradientLowColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
),
GradientMidColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
),
GradientHighColor = list(
ColorScheme = "Gradient",
UseDefaultColorSettings = FALSE
)
),
functionParameterDefaults = c(
plotDefaultsCellPlotColors,
list(
GradientLowColor="#ffffcc",
GradientMidColor="#c2e699",
GradientHighColor="#006837",
OtherPercentage=0,
ColorScheme = "CellPlot",
Measurement = "AgeReadings"
)
)
),
PlotSamplingVariables = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotSamplingVariablesData"
),
PlotPosteriorTraces = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotPosteriorTracesData",
functionArgumentHierarchy = list(
Nclust = list(
UseDefaultPlotSettings=FALSE
),
Iter.max = list(
UseDefaultPlotSettings=FALSE
),
Nstart = list(
UseDefaultPlotSettings=FALSE
),
LowerQuant = list(
UseDefaultPlotSettings=FALSE
),
UpperQuant = list(
UseDefaultPlotSettings=FALSE
),
CatLimit = list(
UseDefaultPlotSettings=FALSE
),
Legend = list(
UseDefaultPlotSettings=FALSE
)
),
functionParameterDefaults = list(
Nclust=4,
Iter.max=20,
Nstart=10,
LowerQuant=.05,
UpperQuant=.95,
CatLimit=30,
Parameter="TotalCatch"
)
),
PlotCatchAtAgeTotals = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotCatchAtAgeTotalsData"
),
PlotMeanWeightAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotMeanWeightAtAgeData"
),
PlotMeanLengthAtAge = list(
functionType = "modelData",
functionCategory = "report",
functionOutputDataType = "PlotMeanLengthAtAgeData"
)
)
#' Define the process property formats for inclusion in stox UI
#'
#' @export
#'
processPropertyFormats <- list(
filePath = list(
class = "single",
title = "The path to a single file"
),
periodvector = list(
class = "vector",
title = "Period defintinions. Start date on the form \"DD-MM\" or \"DD-MM-YYYY\"",
variableTypes = "character"
),
randomcovariates = list(
class = "vector",
title = "One or more variables to use as covariates in Reca",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[!(possibleValues %in% c("CruiseKey", "StationKey", "HaulKey", "SpeciesCategoryKey", "SampleKey"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
stratificationcolumns = list(
class = "vector",
title = "One or more variables to use as stratification columns",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualstratificationcolumns = list(
class = "vector",
title = "One or more variables to use as stratification columns",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualdomainvariables = list(
class = "vector",
title = "One or more variables to use for domain definitions",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- possibleValues[!(possibleValues) %in% c("CruiseKey", "HaulKey", "IndividualKey", "SampleKey", "SpeciesCategoryKey", "StationKey")]
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
psudomainvariables = list(
class = "vector",
title = "One or more variables to use for domain definitions",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- possibleValues[!(possibleValues) %in% c("CruiseKey", "HaulKey", "IndividualKey", "SampleKey", "SpeciesCategoryKey", "StationKey")]
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
individualparameters = list(
class = "vector",
title = "One or more parameter to included in the design specification",
possibleValues = function(StoxBioticData) {
possibleValues <- unique(names(StoxBioticData$Individual))
return(sort(possibleValues))
},
variableTypes = "character"
),
individualnumericvariables = list(
class = "vector",
title = "One or more numeric variables",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Individual")){
for (nn in names(StoxBioticData[[n]])){
if (is.numeric(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
},
variableTypes = "character"
),
samplingunitid = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable to use for sampling unit identification (choose only one)",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
datarecordid = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable to use for data record identification (choose only one)",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Cruise", "Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
weightvariableratioestimate = list(
class = "vector", #convert to class single, if that becomes available.
title = "Variable that represent weight of individuals in grams (choose only one)",
possibleValues = function(AnalyticalPopulationEstimateData) {
possibleValues <- unique(AnalyticalPopulationEstimateData$Variables$Variable)
return(sort(possibleValues))
},
variableTypes = "character"
),
GroupingVariablesSop = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(ReportFdaCatchAtAgeData, ReportFdaWeightAtAgeData, StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[(names(StoxLandingData$Landing) %in% ReportFdaCatchAtAgeData$GroupingVariables$GroupingVariables) &
(names(StoxLandingData$Landing) %in% ReportFdaWeightAtAgeData$GroupingVariables$GroupingVariables)]
return(sort(possibleValues))
},
variableTypes = "character"
),
GroupingVariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[!(names(StoxLandingData$Landing) %in% c("RoundWeight"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
samplereportvariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData, StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[possibleValues %in% names(StoxLandingData$Landing)]
return(sort(possibleValues))
},
variableTypes = "character"
),
onlysamplereportvariables = list(
class = "vector",
title = "One or more variables to use as sampling variables.",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in names(StoxBioticData)){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
return(sort(possibleValues))
},
variableTypes = "character"
),
landingsreportvariables = list(
class = "vector",
title = "One or more variables to use as aggregation variables.",
possibleValues = function(StoxLandingData) {
possibleValues <- names(StoxLandingData$Landing)[names(StoxLandingData$Landing) != "Rundvekt"]
return(sort(possibleValues))
},
variableTypes = "character"
),
fixedcovariates = list(
class = "vector",
title = "One or more variables to use as covariates in Reca",
possibleValues = function(StoxLandingData, StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[possibleValues %in% names(StoxLandingData$Landing)]
return(sort(possibleValues))
},
variableTypes = "character"
),
carcovariate = list(
class = "vector", #convert to class single, if that becomes available.
title = "A variable (choose only one) to use for the CAR variable in Reca.",
possibleValues = function(StoxBioticData) {
possibleValues <- c()
for (n in c("Station", "Haul", "SpeciesCategory", "Sample")){
for (nn in names(StoxBioticData[[n]])){
if (is.character(StoxBioticData[[n]][[nn]]) | is.factor(StoxBioticData[[n]][[nn]]) | is.integer(StoxBioticData[[n]][[nn]])){
possibleValues <- c(possibleValues, nn)
}
}
}
possibleValues <- unique(possibleValues)
possibleValues <- possibleValues[!(possibleValues %in% c("CruiseKey", "StationKey", "HaulKey", "SpeciesCategoryKey", "SampleKey"))]
return(sort(possibleValues))
},
variableTypes = "character"
),
columnvariablecellplot = list(
class = "vector", #convert to class single, if that becomes available.
title = "A variable (choose only one) to use for columns in cell plot.",
possibleValues = function(ReportFdaSamplingData) {
possibleValues <- ReportFdaSamplingData$GroupingVariables$GroupingVariables
return(sort(possibleValues))
},
variableTypes = "character"
),
stratificationvariableslandings = list(
class = "vector",
title = "One or more Stratificaion variable to match with landings",
possibleValues = function(AnalyticalPopulationEstimateData, StoxLandingData){
pv <- names(AnalyticalPopulationEstimateData$StratificationVariables)
pv <- pv[pv != "Stratum"]
pv <- pv[pv %in% names(StoxLandingData$Landing)]
return(pv)
},
variableTypes = "character"
),
domainvariableslandings = list(
class = "vector",
title = "Zero or more Domain variables to match with landings",
possibleValues = function(AnalyticalPopulationEstimateData, StoxLandingData){
pv <- names(AnalyticalPopulationEstimateData$DomainVariables)
pv <- pv[pv != "Domain"]
pv <- pv[pv %in% names(StoxLandingData$Landing)]
return(pv)
},
variableTypes = "character"
),
domainvariablecatchatlength = list(
class = "vector", #convert to class single, if that becomes available.
title = "Exactly on Domain variable to specify length group",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- names(AnalyticalPopulationEstimateData$DomainVariables)
pv <- pv[pv != "Domain"]
return(pv)
},
variableTypes = "character"
),
sourcestratum = list(
class = "vector", #convert to class single, if that becomes available.
title = "Exactly one stratum to use for sampling-frame extension",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum)
return(pv)
},
variableTypes = "character"
),
stratificationvariablesvector = list(
class = "vector",
title = "Names of stratification variables to add",
variableTypes = "character"
),
stratificationvariablestable = list(
class = "table",
title = "Table of Stratification variables for each Stratum",
columnNames = function(StratificationVariables) {
c("Stratum", StratificationVariables)
},
variableTypes = function(StratificationVariables) {
rep("character", 1 + length(StratificationVariables))
}
),
retainstrata = list(
class = "vector",
title = "Strata to retain from aggregation",
possibleValues = function(AnalyticalPopulationEstimateData){
pv <- unique(AnalyticalPopulationEstimateData$StratificationVariables$Stratum)
return(pv)
},
variableTypes = "character"
),
collapsestrataretain = list(
class = "vector",
title = "Strata to retain from collapse",
possibleValues = function(IndividualSamplingParametersData){
pv <- names(IndividualSamplingParametersData$StratificationVariables)
pv <- pv[!(pv %in% c("Stratum", "SampleId"))]
return(pv)
},
variableTypes = "character"
)
)
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