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# CLASSES DEFINITION AND INITIALIZATION
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# Import classes ===============================================================
#' @importClassesFrom aion TimeSeries
#' @importClassesFrom aion RataDie
#' @importClassesFrom dimensio CA
NULL
# MeanDate =====================================================================
#' Mean Date
#'
#' An S4 class to store the weighted mean date (e.g. Mean Ceramic Date) of
#' archaeological assemblages.
#' @slot dates A length-\eqn{p} [`numeric`] vector giving the dates of the
#' (ceramic) types expressed in *[rata die][aion::RataDie-class]*.
#' @slot replications A `numeric` [`matrix`] giving the replications.
#' @section Coerce:
#' In the code snippets below, `x` is a `MeanDate` object.
#' \describe{
#' \item{`as.data.frame(x)`}{Coerces to a [`data.frame`].}
#' }
#' @note
#' This class inherits from [`aion::TimeSeries-class`]: dates are internally
#' stored as *[rata die][aion::RataDie-class]*.
#' @seealso [`aion::TimeSeries-class`]
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases MeanDate-class
#' @keywords internal
.MeanDate <- setClass(
Class = "MeanDate",
slots = c(
dates = "RataDie"
),
contains = "TimeSeries"
)
.SimulationMeanDate <- setClass(
Class = "SimulationMeanDate",
slots = c(
replications = "matrix"
),
contains = "MeanDate"
)
# EventDate ====================================================================
#' Date Model
#'
#' An S4 class to store the event and accumulation times of archaeological
#' assemblages.
#' @slot dates A length-\eqn{m} [`numeric`] vector of dates expressed in
#' *[rata die][aion::RataDie-class]*.
#' @slot model A [multiple linear model][stats::lm()]: the Gaussian
#' multiple linear regression model fitted for event date estimation and
#' prediction.
#' @slot keep An [`integer`] vector giving the subscripts of the CA components
#' to keep.
#' @section Extract:
#' In the code snippets below, `x` is an `EventDate` object.
#' \describe{
#' \item{[`time(x)`][time()]}{Extract dates of assemblages.}
#' \item{[`coef(x)`][coef()]}{Extract model coefficients.}
#' \item{[`fitted(x)`][fitted()]}{Extract model fitted values.}
#' \item{[`residuals(x)`][residuals()]}{Extract model residuals.}
#' \item{[`sigma(x)`][sigma()]}{Extract the residual standard deviation.}
#' \item{[`terms(x)`][terms()]}{Extract model terms.}
#' }
#' @note
#' Dates are internally stored as *[rata die][aion::RataDie-class]*.
#' This class inherits from [`dimensio::CA-class`].
#' @seealso [`dimensio::CA-class`]
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases EventDate-class
#' @keywords internal
.EventDate <- setClass(
Class = "EventDate",
slots = c(
dates = "RataDie",
model = "lm",
keep = "integer"
),
contains = "CA"
)
# AoristicSum ==================================================================
#' Aoristic Sum
#'
#' An S4 class to represent an aoristic analysis results.
#' @slot breaks A [`RataDie-class`] vector giving the date break between
#' time-blocks.
#' @slot weights A [`numeric`] vector.
#' @slot groups A [`character`] vector to store the group names (if any).
#' @slot p A [`numeric`] [`array`] giving the aorisitic probabilities.
#' @section Coerce:
#' In the code snippets below, `x` is an `AoristicSum` object.
#' \describe{
#' \item{`as.data.frame(x)`}{Coerces to a [`data.frame`].}
#' }
#' @note
#' This class inherits from [`aion::TimeSeries-class`]: dates are internally
#' stored as *[rata die][aion::RataDie-class]*.
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases AoristicSum-class
#' @keywords internal
.AoristicSum <- setClass(
Class = "AoristicSum",
slots = c(
breaks = "RataDie",
weights = "numeric",
groups = "character",
p = "array"
),
contains = "TimeSeries"
)
#' Rate of Change
#'
#' An S4 class to represent rates of change from an aoristic analysis.
#' @slot replicates A non-negative [`integer`] giving the number of
#' replications.
#' @slot groups A [`character`] vector to store the group names (if any).
#' @section Coerce:
#' In the code snippets below, `x` is an `AoristicSum` object.
#' \describe{
#' \item{`as.data.frame(x)`}{Coerces to a [`data.frame`].}
#' }
#' @note
#' This class inherits from [`aion::TimeSeries-class`]: dates are internally
#' stored as *[rata die][aion::RataDie-class]*.
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases RateOfChange-class
#' @keywords internal
.RateOfChange <- setClass(
Class = "RateOfChange",
slots = c(
replicates = "integer",
groups = "character"
),
contains = "TimeSeries"
)
# CountApportion ===============================================================
#' Count Apportioning
#'
#' An S4 class to represent an artifact apportioning results. Gives the
#' apportioning of artifact types (columns) per site (rows) and per period
#' (dim. 3).
#' @slot .Data An \eqn{m \times p \times k}{m x p x k} [`array`] giving the
#' proportion of an artifact type (\eqn{p}) for a given period (\eqn{k}).
#' @slot p An \eqn{m \times p \times k}{m x p x k} [`array`] giving the
#' probability of apportioning an artifact type (\eqn{p}) to a given period
#' (\eqn{k}).
#' @slot method A [`character`] string specifying the distribution used for
#' apportioning (type popularity curve).
#' @slot from A length-one [`numeric`] vector giving the beginning of the
#' period of interest (in years AD).
#' @slot to A length-one [`numeric`] vector giving the end of the period of
#' interest (in years AD).
#' @slot step A length-one [`numeric`] vector giving the step size, i.e. the
#' width of each time step for apportioning (in years AD).
#' @note This class inherits from base [`array`].
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases CountApportion-class
#' @keywords internal
.CountApportion <- setClass(
Class = "CountApportion",
slots = c(
p = "array",
method = "character",
from = "numeric",
to = "numeric",
step = "numeric"
),
contains = "array"
)
# IncrementTest ================================================================
#' Frequency Increment Test
#'
#' An S4 class to represent a Frequency Increment Test results.
#' @slot statistic A [`numeric`] vector giving the values of the t-statistic.
#' @slot parameter An [`integer`] giving the degrees of freedom for the
#' t-statistic.
#' @slot p_value A [`numeric`] vector giving the the p-value for the test.
#' @section Coerce:
#' In the code snippets below, `x` is an `IncrementTest` object.
#' \describe{
#' \item{`as.data.frame(x)`}{Coerces to a [`data.frame`].}
#' }
#' @note
#' This class inherits from [`aion::TimeSeries-class`]: dates are internally
#' stored as *[rata die][aion::RataDie-class]*.
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases IncrementTest-class
#' @keywords internal
.IncrementTest <- setClass(
Class = "IncrementTest",
slots = c(
statistic = "numeric",
parameter = "integer",
p_value = "numeric"
),
contains = "TimeSeries"
)
# PermutationOrder =============================================================
#' Permutation Order
#'
#' S4 classes to represent a permutation order.
#' @slot rows_order An [`integer`] vector giving the rows permutation.
#' @slot columns_order An [`integer`] vector giving the columns permutation.
#' @section Subset:
#' In the code snippets below, `x` is a `PermutationOrder` object.
#' \describe{
#' \item{`x[[i]]`}{Extract information from a slot selected by subscript `i`.
#' `i` is a length-one character vector.}
#' }
#' @seealso [`dimensio::CA-class`]
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases PermutationOrder-class
#' @keywords internal
.PermutationOrder <- setClass(
Class = "PermutationOrder",
slots = c(
rows_order = "integer",
columns_order = "integer"
),
contains = "VIRTUAL"
)
#' @rdname PermutationOrder-class
#' @aliases RankPermutationOrder-class
.RankPermutationOrder <- setClass(
Class = "RankPermutationOrder",
contains = "PermutationOrder"
)
#' @rdname PermutationOrder-class
#' @aliases AveragePermutationOrder-class
.AveragePermutationOrder <- setClass(
Class = "AveragePermutationOrder",
contains = c("PermutationOrder", "CA")
)
# RefineCA =====================================================================
#' Partial Bootstrap CA
#'
#' An S4 class to store partial bootstrap correspondence analysis results.
#' @slot length A [`numeric`] vector giving the convex hull maximum
#' dimension length.
#' @slot cutoff A length-one [`numeric`] vector giving the cutoff value for
#' samples selection.
#' @slot keep An [`integer`] vector giving the subscript of the variables
#' to be kept.
#' @author N. Frerebeau
#' @family classes
#' @docType class
#' @aliases RefineCA-class
#' @keywords internal
.RefinePermutationOrder <- setClass(
Class = "RefinePermutationOrder",
slots = c(
length = "numeric",
cutoff = "numeric",
keep = "integer",
margin = "integer"
),
contains = "AveragePermutationOrder"
)
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