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ProjKrigSpTi.R
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#' #' Spatio temporal interpolation using projected spatial temporal normal model.
#'
#' \code{ProjKrigSpTi} function computes the spatio-temporal
#' prediction for circular space-time data using samples
#' from the posterior distribution of the space-time projected normal model.
#'
#'
#' @param ProjSpTi_out the functions takes the output of \code{\link{ProjSpTi}} function
#' @param coords_obs coordinates of observed locations (in UTM)
#' @param coords_nobs coordinates of unobserved locations (in UTM)
#' @param times_obs numeric vector of observed time coordinates
#' @param times_nobs numeric vector of unobserved time coordinates
#' @param x_obs observed values in \eqn{[0,2\pi)}
#' If they are not in \eqn{[0,2\pi)}, the function will tranform
#' the data in the right interval
#' @return a list of 3 elements
#' \describe{
#' \item{\code{M_out}}{the mean of the associated linear process on the prediction locations coords_nobs (rows) over all the posterior samples (columns) returned by ProjSpTi}
#' \item{\code{V_out}}{the variance of the associated linear process on the prediction locations coords_nobs (rows) over all the posterior samples (columns) returned by ProjSpTi}
#' \item{\code{Prev_out}}{are the posterior predicted values at the unobserved locations.}
#' }
#'
#' @family spato-temporal interpolations
#' @seealso \code{\link{ProjSpTi}} to sample the posterior distribution of the spatio-temporal
#' Projected Normal model,
#' \code{\link{WrapSpTi}} to sample the posterior distribution of the spatio-temporal
#' Wrapped Normal model and \code{\link{WrapKrigSpTi}} for
#' spatio-temporal interpolation under the same model
#' @references G. Mastrantonio, G.Jona Lasinio,
#' A. E. Gelfand, "Spatio-temporal circular models with
#' non-separable covariance structure", TEST 25 (2016), 331–350.
#' @references F. Wang, A. E. Gelfand,
#' "Modeling space and space-time directional data using projected Gaussian processes",
#' Journal of the American Statistical Association,109 (2014), 1565-1580
#' @references T. Gneiting, "Nonseparable, Stationary Covariance Functions for Space-Time
#' Data", JASA 97 (2002), 590-600
#' @examples
#' library(CircSpaceTime)
#' #### simulated example
#' ## auxiliary functions
#' rmnorm <- function(n = 1, mean = rep(0, d), varcov) {
#' d <- if (is.matrix(varcov)) {
#' ncol(varcov)
#' } else {
#' 1
#' }
#' z <- matrix(rnorm(n * d), n, d) %*% chol(varcov)
#' y <- t(mean + t(z))
#' return(y)
#' }
#' ####
#' # Simulation using a gneiting covariance function
#' ####
#' set.seed(1)
#' n <- 20
#'
#' coords <- cbind(runif(n, 0, 100), runif(n, 0, 100))
#' coordsT <- cbind(runif(n, 0, 100))
#' Dist <- as.matrix(dist(coords))
#' DistT <- as.matrix(dist(coordsT))
#'
#' rho <- 0.05
#' rhoT <- 0.01
#' sep_par <- 0.1
#' sigma2 <- 1
#' alpha <- c(0.5)
#' SIGMA <- sigma2 * (rhoT * DistT^2 + 1)^(-1) * exp(-rho * Dist / (rhoT * DistT^2 + 1)^(sep_par / 2))
#' tau <- 0.2
#'
#' Y <- rmnorm(
#' 1, rep(alpha, times = n),
#' kronecker(SIGMA, matrix(c(sigma2, sqrt(sigma2) * tau, sqrt(sigma2) * tau, 1), nrow = 2))
#' )
#' theta <- c()
#' for (i in 1:n) {
#' theta[i] <- atan2(Y[(i - 1) * 2 + 2], Y[(i - 1) * 2 + 1])
#' }
#' theta <- theta %% (2 * pi) ## to be sure we have values in (0,2pi)
#' rose_diag(theta)
#' ################ some useful quantities
#' rho_sp.min <- 3 / max(Dist)
#' rho_sp.max <- rho_sp.min + 0.5
#' rho_t.min <- 3 / max(DistT)
#' rho_t.max <- rho_t.min + 0.5
#' ### validation set 20% of the data
#' val <- sample(1:n, round(n * 0.2))
#'
#' set.seed(200)
#'
#' mod <- ProjSpTi(
#' x = theta[-val],
#' coords = coords[-val, ],
#' times = coordsT[-val],
#' start = list(
#' "alpha" = c(0.66, 0.38, 0.27, 0.13),
#' "rho_sp" = c(0.29, 0.33),
#' "rho_t" = c(0.25, 0.13),
#' "sep_par" = c(0.56, 0.31),
#' "tau" = c(0.71, 0.65),
#' "sigma2" = c(0.47, 0.53),
#' "r" = abs(rnorm(length(theta[-val])))
#' ),
#' priors = list(
#' "rho_sp" = c(rho_sp.min, rho_sp.max), # Uniform prior in this interval
#' "rho_t" = c(rho_t.min, rho_t.max), # Uniform prior in this interval
#' "sep_par" = c(1, 1), # Beta distribution
#' "tau" = c(-1, 1), ## Uniform prior in this interval
#' "sigma2" = c(10, 3), # inverse gamma
#' "alpha_mu" = c(0, 0), ## a vector of 2 elements,
#' ## the means of the bivariate Gaussian distribution
#' "alpha_sigma" = diag(10, 2) # a 2x2 matrix, the covariance matrix of the
#' # bivariate Gaussian distribution,
#' ),
#' sd_prop = list(
#' "sep_par" = 0.1, "sigma2" = 0.1, "tau" = 0.1, "rho_sp" = 0.1, "rho_t" = 0.1,
#' "sdr" = sample(.05, length(theta), replace = TRUE)
#' ),
#' iter = 4000,
#' BurninThin = c(burnin = 2000, thin = 2),
#' accept_ratio = 0.234,
#' adapt_param = c(start = 155000, end = 156000, exp = 0.5),
#' n_chains = 2,
#' parallel = TRUE,
#' )
#' check <- ConvCheck(mod)
#' check$Rhat ### convergence has been reached when the values are close to 1
#' #### graphical checking
#' #### recall that it is made of as many lists as the number of chains and it has elements named
#' #### as the model's parameters
#' par(mfrow = c(3, 3))
#' coda::traceplot(check$mcmc)
#' par(mfrow = c(1, 1))
#' # once convergence is reached we run the interpolation on the validation set
#' Krig <- ProjKrigSpTi(
#' ProjSpTi_out = mod,
#' coords_obs = coords[-val, ],
#' coords_nobs = coords[val, ],
#' times_obs = coordsT[-val],
#' times_nobs = coordsT[val],
#' x_obs = theta[-val]
#' )
#'
#' #### checking the prediction
#' \donttest{
#' Proj_ape <- APEcirc(theta[val], Krig$Prev_out)
#' Proj_crps <- CRPScirc(theta[val],Krig$Prev_out)
#' }
#' @export
ProjKrigSpTi <- function(
ProjSpTi_out,
coords_obs,
coords_nobs,
times_obs,
times_nobs,
x_obs) {
x_obs <- x_obs %% (2 * pi)
## ## ## ## ## ## ##
## Posterior samples
## ## ## ## ## ## ##
AppName <- names(ProjSpTi_out[[1]])
AppName[1] <- "rstar"
names(ProjSpTi_out[[1]]) <- AppName
pp <- unlist(ProjSpTi_out)
pp <- unlist(ProjSpTi_out)
sigma2 <- as.numeric(pp[regexpr("sigma2", names(pp)) == 1])
rho_sp <- as.numeric(pp[regexpr("rho_sp", names(pp)) == 1])
rho_t <- as.numeric(pp[regexpr("rho_t", names(pp)) == 1])
sep_par <- as.numeric(pp[regexpr("sep_par", names(pp)) == 1])
tau <- as.numeric(pp[regexpr("tau", names(pp)) == 1])
row.r <- nrow(ProjSpTi_out[[1]]$rstar)
pp2 <- as.numeric(pp[regexpr("rstar", names(pp)) == 1])
r <- matrix(pp2, nrow = row.r)
row.alpha <- nrow(ProjSpTi_out[[1]]$alpha)
pp2 <- as.numeric(pp[regexpr("alpha", names(pp)) == 1])
alpha <- matrix(pp2, nrow = row.alpha)
rm(pp, pp2)
## ## ## ## ## ## ##
## Indices
## ## ## ## ## ## ##
n <- nrow(r)
nprev <- nrow(coords_nobs)
nsample <- ncol(r)
## ## ## ## ## ## ##
## Distance matrix for observed and non observed data
## ## ## ## ## ## ##
H_tot <- as.matrix(stats::dist(rbind(coords_obs, coords_nobs)))
Ht_tot <- as.matrix(stats::dist(c(times_obs, times_nobs)))
## ## ## ## ## ## ##
## Model estimation
## ## ## ## ## ## ##
out <- ProjKrigSpTiCpp(sigma2, rho_sp, tau, alpha, r, n, nsample, H_tot, Ht_tot, nprev, x_obs, rho_t, sep_par)
out$Prev_out <- out$Prev_out %% (2 * pi)
return(out)
}