cascsim: Casualty Actuarial Society Individual Claim Simulator

# This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0.
# If a copy of the MPL was not distributed with this file, You can obtain one at https://mozilla.org/MPL/2.0/.

#' setID
#' Set the ID for an object
#' @name setID<-
#' @param this Self
#' @param ... Additional function arguments
#' @param value ID
#' @examples
#' xindex <- new("Index")
#' setID(xindex) <- "IDX1"
#' xindex@indexID
#' @rdname setID-methods
#' @exportMethod setID<-
setGeneric("setID<-", function(this, ..., value) standardGeneric("setID<-"))

#' Set the start date for the claim simulation exercise
#' @name setStartDate<-
#' @param this Self
#' @param ... Additional function arguments
#' @param value Start Date
#' @rdname setStartDate-methods
setGeneric("setStartDate<-", function(this, ..., value) standardGeneric("setStartDate<-"))

#' Input the raw data.
#' @name setObservation<-
#' @param this FitDist Object or Copula Object
#' @param value A data frame or a matrix. For FitDist object, it could be a two-column data frame with the occurrence date and loss size/number of occurrence. Or a one-column data frame with loss size (ifreq == FALSE) or number of occurrence (ifreq == TRUE && idate == FALSE) or occurrence dates (ifreq == TRUE && idate == TRUE). For Copula object, it could be a matrix with each column contains the experience data of a variable.
#' @examples
#' library(cascsim)
#' dist1 <- new("Pareto", p1 = 20, p2 = 3)
#' dist2 <- new("Normal", p1 = 5, p2 = 3, min = 0, max = 20, truncated = TRUE)
#' nom.cop <- new("CopulaObj", param = c(0.5), marginal = list(dist1 = dist1, dist2 = dist2), dimension = 2)
#' setObservation(nom.cop) <- copulaSample(nom.cop, 100)
#' nom.cop@observation
#' @rdname setObservation-methods
#' @export setObservation<-
setGeneric("setObservation<-", function(this, value) standardGeneric("setObservation<-"))

#' Density function.
#' @name Density
#' @param object Distribution Object
#' @param x Variable value
#' @param ... Additional function arguments
#' @examples
#' xPareto <- new("Pareto", p1 = 20, p2 = 3)
#' Density(xPareto, 50)
#' @rdname Density-methods
#' @exportMethod Density
setGeneric("Density", function(object, x, ...) standardGeneric("Density"))

#' Probability function.
#' @name Probability
#' @param object Distribution Object
#' @param q Variable value
#' @param ... Additional function arguments
#' @examples
#' xPareto <- new("Pareto", p1 = 20, p2 = 3)
#' Probability(xPareto, 50)
#' @rdname Probability-methods
#' @exportMethod Probability
setGeneric("Probability", function(object, q, ...) standardGeneric("Probability"))

#' Quantile function.
#' @name Quantile
#' @param object Distribution Object
#' @param p Probability
#' @param ... Additional function arguments
#' @examples
#' xPareto <- new("Pareto", p1 = 20, p2 = 3)
#' Quantile(xPareto, 0.6)
#' @rdname Quantile-methods
#' @export Quantile
setGeneric("Quantile", function(object, p, ...) standardGeneric("Quantile"))

#' Plot function.
#' @name doPlot
#' @param object Object
#' @param ... Additional function arguments
#' @examples
#' xPareto <- new("Pareto", p1 = 20, p2 = 3)
#' doPlot(xPareto)
#' @rdname doPlot-methods
#' @exportMethod doPlot
setGeneric("doPlot", function(object, ...) standardGeneric("doPlot"))

#' Get input data from an object.
#' @name getObservation
#' @param object Object
#' @param ... Additional function arguments
#' @examples
#' library(cascsim)
#' data(claimdata)
#'
#' # frequecy fitting example
#' findex <- new("Index", startDate = as.Date("2012-01-01"), tabulate = TRUE, monthlyIndex = c(
#'   rep(1, 11),
#'   cumprod(c(1, rep(1.5^(1 / 12), 11))), cumprod(c(1.5, rep((1.3 / 1.5)^(1 / 12), 11))),
#'   cumprod(c(1.3, rep((1.35 / 1.3)^(1 / 12), 11))), cumprod(c(1.35, rep((1.4 / 1.35)^(1 / 12), 11))), 1.4
#' ))
#' rawdata <- as.data.frame(as.Date(claimdata[(claimdata[, "LoB"] == "Auto" &
#'   claimdata[, "Type"] == "H"), ]$occurrenceDate))
#' colnames(rawdata) <- "occurrenceDate"
#' xFit <- new("FitDist",
#'   observation = rawdata, trend = findex, startDate = as.Date("2012-01-01"),
#'   method = "mle", ifreq = TRUE, idate = TRUE, freq = "Monthly"
#' )
#' getObservation(xFit)
#' @rdname getObservation-methods
#' @exportMethod getObservation
setGeneric("getObservation", function(object, ...) standardGeneric("getObservation"))

#' Get the trend index.
#' @name getTrend
#' @param object Object
#' @param ... Additional function arguments
#' @examples
#' library(cascsim)
#' data(claimdata)
#'
#' # frequecy fitting example
#' findex <- new("Index", startDate = as.Date("2012-01-01"), tabulate = TRUE, monthlyIndex = c(
#'   rep(1, 11),
#'   cumprod(c(1, rep(1.5^(1 / 12), 11))), cumprod(c(1.5, rep((1.3 / 1.5)^(1 / 12), 11))),
#'   cumprod(c(1.3, rep((1.35 / 1.3)^(1 / 12), 11))), cumprod(c(1.35, rep((1.4 / 1.35)^(1 / 12), 11))), 1.4
#' ))
#' rawdata <- as.data.frame(as.Date(claimdata[(claimdata[, "LoB"] == "Auto" &
#'   claimdata[, "Type"] == "H"), ]$occurrenceDate))
#' colnames(rawdata) <- "occurrenceDate"
#' xFit <- new("FitDist",
#'   observation = rawdata, trend = findex, startDate = as.Date("2012-01-01"),
#'   method = "mle", ifreq = TRUE, idate = TRUE, freq = "Monthly"
#' )
#' getTrend(xFit)
#' @rdname getTrend-methods
#' @exportMethod getTrend
setGeneric("getTrend", function(object, ...) standardGeneric("getTrend"))