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#' Conditional multivariate extreme values modelling
#'
#' Fit the conditional multivariate extreme value model of Heffernan and Tawn
#'
#' The function \code{mex} works as follows. First, Generalized Pareto
#' distributions (GPD) are fitted to the upper tails of each of the marginal
#' distributions of the data: the GPD parameters are estimated for each column
#' of the data in turn, independently of all other columns. Then, the
#' conditional multivariate approach of Heffernan and Tawn is used to model the
#' dependence between variables. The returned object is of class "mex".
#'
#' This function is a wrapper for calls to \code{\link{migpd}} and
#' \code{\link{mexDependence}}, which estimate parameters of the marginal and
#' dependence components of the Heffernan and Tawn model respectively. See
#' documentation of these functions for details of modelling issues including
#' the use of penalties / priors, threshold choice and checking for convergence
#' of parameter estimates.
#'
#' The \code{plot} method produces diagnostic plots for the fitted dependence
#' model described by Heffernan and Tawn, 2004. The plots are best viewed by
#' using the plotting area split by \code{par(mfcol=c(.,.))} rather than
#' \code{mfrow}, see examples below. Three diagnostic plots are produced for
#' each dependent variable:
#'
#' 1) Scatterplots of the residuals Z from the fitted model of Heffernan and
#' Tawn (2004) are plotted against the quantile of the conditioning variable,
#' with a lowess curve showing the local mean of these points. 2) The absolute
#' value of \code{Z-mean(Z)} is also plotted, again with the lowess curve
#' showing the local mean of these points. Any trend in the location or
#' scatter of these variables with the conditioning variable indicates a
#' violation of the model assumption that the residuals Z are indepenendent of
#' the conditioning variable. This can be indicative of the dependence
#' threshold used being too low. 3) The final plots show the original data (on
#' the original scale) and the fitted quantiles (specified by \code{quantiles})
#' of the conditional distribution of each dependent variable given the
#' conditioning variable. A model that fits well will have good agreement
#' between the distribution of the raw data (shown by the scatter plot) and the
#' fitted quantiles. Note that the raw data are a sample from the joint
#' distribution, whereas the quantiles are those of the estimated conditional
#' distribution given the value of the conditioning variable, and while these
#' two distributions should move into the same part of the sample space as the
#' conditioning variable becomes more extreme, they are not the same thing!
#'
#' The \code{predict} method for \code{mex} works as follows. The returned
#' object has class "predict.mex". Simulated values of the dependent variables
#' are created, given that the conditioning variable is above its 100\code{pqu}
#' quantile. If \code{predict.mex} is passed an object \code{object} of class
#' \code{"mex"} then the simulated values are based only on the point estimate
#' of the dependence model parameters, and the original data. If
#' \code{predict.mex} is passed an object \code{object} of class
#' \code{"bootmex"} then the returned value additionally contains simulated
#' replicate data sets corresponding to the bootstrap model parameter
#' estimates. In both cases, the simulated values based on the original data
#' and point estimates appear in component \code{object$data$simulated}. The
#' simulated data from the bootstrap estimates appear in
#' \code{object$replicates}.
#'
#' The \code{plot} method for class \code{"predict.mex"} displays both the
#' original data and the simulated data generated above the threshold for
#' prediction; it shows the threshold for prediction (vertical line) and also
#' the curve joining equal quantiles of the marginal distributions -- this is
#' for reference: variables that are perfectly dependent will lie exactly on
#' this curve. Original data are shown with one plotting character and
#' simulated data with another; colours of simulated point distinguish those
#' points which have the conditioning variable as the largest (on a quantile
#' scale) or not the largest.
#'
#' The function \code{mexAll} fits a collection of GPD and conditional
#' dependence models, the same fitted GPD being used for all of the dependence
#' model fits. This can be used in turn to generate Monte Carlo samples from
#' the entire sample space usign the collected dependence models.
#'
#' @aliases mex plot.mex print.mex predict.mex summary.predict.mex plot.predict.mex ggplot.mex
#' mexAll print.mexList print.summary.mex summary.mex
#' @param data A numeric matrix or data.frame, the columns of which are to be
#' modelled.
#' @param which The variable on which to condition. This can be either scalar,
#' indicating the column number of the conditioning variable, or character,
#' giving the column name of the conditioning variable.
#' @param mth Marginal thresholds. In \code{mex}, the threshold above which to
#' fit generalized Pareto distributions. If this is a vector of length 1, the
#' same threshold will be used for each variable. Otherwise, it should be a
#' vector whose length is equal to the number of columns in \code{data}.
#'
#' In \code{summary.predict.mex}, the thresholds over which to simulate data
#' from the fitted multivariate model. If not supplied, it is taken to be the
#' thresholds that were used to fit the dependence model on the scale of the
#' original data.
#' @param mqu Marginal quantiles As an alternative to specifying the marginal
#' GPD fitting thresholds via \code{mth}, you can specify the quantile (a
#' probability) above which to fit generalized Pareto distributions. If this is
#' a vector of length 1, the same quantile will be used for each variable.
#' Otherwise, it should be a vector whose length is equal to the number of
#' columns in \code{data}.
#' @param dqu Dependence quantile. Used to specify the quantile at which to
#' threshold the conditioning variable data when estimating the dependence
#' parameters. For example \code{dqu=0.7} will result in the data with the
#' highest 30\% of values of the conditioning variable being used to estimate
#' the dependence parameters. The same threshold will be used for each
#' dependent variable. If not supplied then the default is to set
#' \code{dqu=mqu[which]} the quantile corresponding to the threshold used to
#' fit the marginal model to the tail of the conditioning variable. Note that
#' there is no requirement for the quantiles used for marginal fitting
#' (\code{mqu}) and dependence fitting (\code{dqu}) to be the same, or for them
#' to be ordered in any way.
#' @param margins See documentation for \code{\link{mexDependence}}.
#' @param constrain See documentation for \code{\link{mexDependence}}.
#' @param v See documentation for \code{\link{mexDependence}}.
#' @param penalty How to penalize the likelihood when estimating the marginal
#' generalized Pareto distributions. Defaults to ``gaussian''. See the help
#' file for \code{\link{evm}} for more information.
#' @param maxit The maximum number of iterations to be used by the optimizer.
#' defaults to \code{maxit = 10000}.
#' @param trace Passed internally to \code{\link{optim}}. Whether or not to
#' inform the user of the progress of the optimizer. Defaults to 0, indicating
#' no trace.
#' @param verbose Whether or not to keep the user informed of progress.
#' Defaults to \code{verbose = FALSE}.
#' @param priorParameters Parameters of prior/penalty used for estimation of
#' the GPD parameters. This is only used if \code{penalty = "gaussian"}. It
#' is a named list, each element of which contains two components: the first
#' component should be a vector of length 2 corresponding to the location of
#' the Gaussian distribution; the second a 2x2 matrix corresponding to the
#' covariance matrix of the distribution. The names should match the names of
#' the columns of \code{data}. If not provided, the default priors are
#' independent normal, centred at zero, with variance 10000 for phi=log(sigma)
#' and 0.25 for xi. See the details section.
#' @param quantiles A vector of quantiles taking values between 0 and 1
#' specifying the quantiles of the conditional distributions which will be
#' plotted.
#' @param col In \code{plot} method for objects of class \code{mex}, the colour
#' for points on scatterplots of residuals and original data respectively. In
#' \code{plot} method for objects of class \code{predict.mex}, the colours of
#' points for observed, and simulated data (conditioning variable not the
#' largest) and simulated data (conditioning variable is the largest)
#' respectively.
#' @param x,object Object of class \code{mex} or \code{summary.mex} as returned by these functions respectively.
#' @param pqu Prediction quantile. Argument to \code{predict} method. The
#' quantile of the conditioning variable above which it will be simulated for
#' importance sampling based prediction. Defaults to \code{pqu = .99 }.
#' @param smoothZdistribution In \code{predict.mex}, whether or not to sample
#' from the smoothed distribution of the underlying residuals. Defaults to
#' \code{FALSE}, in which case no smoothing is carried out. If \code{TRUE}
#' then each margin of the underlying multivariate residual is smoothed
#' independently, by using kernel smoothing with a normal kernel, and bandwith
#' chosen using the \code{\link{bw.nrd}} function. This can be useful for
#' removing "stripeyness" in importance samples which have few values in the
#' conditional tails.
#' @param nsim Argument to \code{predict} method. The number of simulated
#' observations to be generated for prediction.
#' @param probs In \code{summary} method for objects of class
#' \code{predict.mex}: the quantiles of the conditional distribution(s) to
#' calculate. Defaults to 5\%, 50\% and 95\%.
#' @param pch,cex Plotting characters: colours and symbol expansion. The
#' observed and simulated data are plotted using different symbols, controlled
#' by these arguments and \code{col}, each of which should be of length 2.
#' @param ask Whether or not to ask before changing the plot. Defaults to
#' \code{ask = TRUE}.
#' @param shape,size,mapping,ptcol,fill,plot.,environment,xlab,ylab,main Further arguments to plot and ggplot methods.
#' @param ... Further arguments to be passed to methods.
#' @return A call to \code{mex} returns an list of class \code{mex} containing
#' the following three items: \item{margins}{An object of class
#' \code{\link{migpd}}.} \item{dependence}{An object of class
#' \code{\link{mexDependence}}.} \item{call}{This matches the original function
#' call.} There are \code{plot}, \code{summary}, \code{coef} and \code{predict}
#' methods for this class.
#'
#' A call to \code{predict.mex} does the importance sampling for prediction,
#' and returns a list of class \code{"predict.mex"} for which there are print
#' and plot methods available. The summary method for this class of object is
#' intended to be used following a call to the predict method, to estimate
#' quantiles or probabilities of threshold excesses for the fitted conditional
#' distributions given the conditioning variable above the threshold for
#' prediction. See examples below.
#'
#' There are \code{print}, \code{summary} and \code{plot} methods available for
#' the class "predict.mex".
#' @note The package \code{texmex} is equipped to fit GPD models to the upper
#' marginal tails only, not the lower tails. This is appropriate for
#' extrapolating into the tails of any dependent variable when dependence
#' between this variable and the conditioning variable is positive. In the
#' case of negative dependence between the conditioning variable and any
#' dependent variable, estimation of the conditional distribution of the
#' dependent variable for extreme values of the conditioning variable would
#' naturally visit the lower tail of the dependent variable. Extrapolation
#' beyond the range of the observed lower tail is not supported in the current
#' version of \code{texmex}. In cases where negative dependence is observed and
#' extrapolation is required into the lower tail of the dependent variable, the
#' situation is trivially resolved by working with a reflection of the
#' dependent variable (Y becomes -Y and so the upper and lower tails are
#' swapped). Results can be calculated for the reflected variable then
#' reflected back to the correct scale. This is satisfactory when only the
#' pair of variables (the conditioning and single dependent variable) are of
#' interest, but when genuine multivariate (as opposed to simply bivariate)
#' structure is of interest, this approach will destroy the dependence
#' structure between the reflected dependent variable and the remaining
#' dependent variables.
#' @author Harry Southworth, Janet E. Heffernan
#' @seealso \code{\link{migpd}}, \code{\link{mexDependence}},
#' \code{\link{bootmex}}, \code{\link{mexMonteCarlo}}
#' @references J. E. Heffernan and J. A. Tawn, A conditional approach for
#' multivariate extreme values, Journal of the Royal Statistical Society B, 66,
#' 497 - 546, 2004
#' @keywords models multivariate
#' @examples
#'
#' w <- mex(winter, mqu=.7)
#' w
#' par(mfcol=c(3, 2))
#' plot(w)
#'
#' par(mfcol=c(2,2))
#' p <- predict(w)
#' summary(p)
#' summary(p,probs=c(0.01,0.2,0.5,0.8,0.99))
#' summary(p,probs=0.5,mth=c(40,50,150,25,50))
#' p
#' plot(p)
#'
#'
#' @export mex
mex <- function(data, which, mth, mqu, dqu, margins="laplace",constrain=TRUE,v=10,
penalty="gaussian", maxit=10000,
trace=0, verbose=FALSE, priorParameters=NULL){
theCall <- match.call()
if(is.null(colnames(data))){
colnames(data) <- paste(rep("Column",ncol(data)),1:ncol(data),sep="")
}
if (missing(which)){
which <- colnames(data)[1]
message("which not given. Conditioning on", which, "\n")
}
if (missing(mth)){
if(length(mqu) == 1){
mqu <- rep(mqu, ncol(data))
}
if( length(mqu) != ncol(data)){
stop("mqu must be length 1 or length equal to the dimension of the data")
}
mth <- unlist(lapply(1:length(mqu), function(i, data, p){
quantile(data[, i], prob=p[i])
}, p=mqu, data=data))
}
res1 <- migpd(data=data, mth=mth, penalty=penalty,
maxit=maxit, trace=trace, verbose=verbose,
priorParameters=priorParameters)
res2 <- mexDependence(x= res1, which=which, dqu=dqu, margins=margins, constrain=constrain, v=v)
res2$call <- theCall
res2
}
#' @export
print.mex <- function(x, digits=4, ...){
print(x$call, ...)
cat("\n\nMarginal models:\n")
summary(x[[1]],digits=digits)
cat("\nDependence model:\n\n")
print(x[[2]],...)
invisible(x)
}
#' @export
summary.mex <- function(object, ...){
obj <- coef(object)
oldClass(obj) <- "summary.mex"
obj
}
#' @export
print.summary.mex <- function(x, digits=3, ...){
cat("\nMarginal models:\n")
print(x[[1]],digits=digits)
cat("\nDependence model:\n\n")
print(x[[2]],digits=digits)
invisible(x)
}
#' @export
coef.mex <- function(object, ...){
res1 <- coef(object[[1]])
res2 <- coef(object[[2]]) # uses native coef method
list(margins=res1, dependence=res2)
}
```

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