R/gamBiCopCDF.R

In gamCopula: Generalized Additive Models for Bivariate Conditional Dependence Structures and Vine Copulas

#' Conditional distribution function of a Generalized Additive model for the
#' copula parameter or Kendall's tau
#'
#' This function returns the distribution function of a bivariate conditional
#' copula, where either the copula parameter or the Kendall's tau is modeled
#' as a function of the covariates.
#'
#' @param object \code{\link{gamBiCop-class}} object.
#' @param newdata (Same as in \code{\link{predict.gam}} from the
#' \code{\link[mgcv:mgcv-package]{mgcv}} package) A matrix or data frame
#' containing the values of the model covariates at which predictions are
#' required. If this is not provided then the distribution corresponding to the
#' original data are returned. If \code{newdata} is provided then it should contain all
#' the variables needed for prediction: a warning is generated if not.
#' @return The conditional density.
#' @seealso \code{\link{gamBiCop}} and \code{\link{gamBiCopPredict}}.
#' @examples
#' require(copula)
#' set.seed(0)
#'
#' ## Simulation parameters (sample size, correlation between covariates,
#' ## Gaussian copula family)
#' n <- 2e2
#' rho <- 0.5
#' fam <- 1
#'
#' ## A calibration surface depending on three variables
#' eta0 <- 1
#' calib.surf <- list(
#'   calib.quad <- function(t, Ti = 0, Tf = 1, b = 8) {
#'     Tm <- (Tf - Ti) / 2
#'     a <- -(b / 3) * (Tf^2 - 3 * Tf * Tm + 3 * Tm^2)
#'     return(a + b * (t - Tm)^2)
#'   },
#'   calib.sin <- function(t, Ti = 0, Tf = 1, b = 1, f = 1) {
#'     a <- b * (1 - 2 * Tf * pi / (f * Tf * pi +
#'       cos(2 * f * pi * (Tf - Ti))
#'       - cos(2 * f * pi * Ti)))
#'     return((a + b) / 2 + (b - a) * sin(2 * f * pi * (t - Ti)) / 2)
#'   },
#'   calib.exp <- function(t, Ti = 0, Tf = 1, b = 2, s = Tf / 8) {
#'     Tm <- (Tf - Ti) / 2
#'     a <- (b * s * sqrt(2 * pi) / Tf) * (pnorm(0, Tm, s) - pnorm(Tf, Tm, s))
#'     return(a + b * exp(-(t - Tm)^2 / (2 * s^2)))
#'   }
#' )
#'
#' ## 3-dimensional matrix X of covariates
#' covariates.distr <- mvdc(normalCopula(rho, dim = 3),
#'   c("unif"), list(list(min = 0, max = 1)),
#'   marginsIdentical = TRUE
#' )
#' X <- rMvdc(n, covariates.distr)
#' colnames(X) <- paste("x", 1:3, sep = "")
#'
#' ## U in [0,1]x[0,1] with copula parameter depending on X
#' U <- condBiCopSim(fam, function(x1, x2, x3) {
#'   eta0 + sum(mapply(function(f, x)
#'     f(x), calib.surf, c(x1, x2, x3)))
#' }, X[, 1:3], par2 = 6, return.par = TRUE)
#'
#' ## Merge U and X
#' data <- data.frame(U$data, X)
#' names(data) <- c(paste("u", 1:2, sep = ""), paste("x", 1:3, sep = ""))
#'
#' ## Model fit with penalized cubic splines (via min GCV)
#' basis <- c(3, 10, 10)
#' formula <- ~ s(x1, k = basis[1], bs = "cr") +
#'   s(x2, k = basis[2], bs = "cr") +
#'   s(x3, k = basis[3], bs = "cr")
#' system.time(fit <- gamBiCopFit(data, formula, fam))
#'
#' ## Evaluate the conditional density
#' gamBiCopCDF(fit$res)
#' @export
gamBiCopCDF <- function(object, newdata = NULL) {
  par <- gamBiCopPredict(object, newdata, "par")$par
  mm <- object@model

  if (is.null(newdata)) {
    data <- cbind(mm$data[, "u1"], mm$data[, "u2"])
  } else {
    data <- cbind(newdata[, "u1"], newdata[, "u2"])
  }
  data <- cbind(data, par)
  out <- as.numeric(bicoppd1d2(data, object@family, p = FALSE, cdf = TRUE))

  return(out)
}