## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(comment = "#>", collapse = TRUE)

required <- c("evdbayes")

if (!all(unlist(lapply(required, function(pkg) requireNamespace(pkg, quietly = TRUE)))))
  knitr::opts_chunk$set(eval = FALSE)

## ------------------------------------------------------------------------
library(revdbayes)
library(evdbayes)
# Set the number of posterior samples required.
n <- 10000
set.seed(46)

## ------------------------------------------------------------------------
data(gom)
thresh <- quantile(gom, probs = 0.65)
fp <- set_prior(prior = "flat", model = "gp", min_xi = -1)

## ------------------------------------------------------------------------
t1 <- system.time(
gp1 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom,
             rotate = FALSE)
)[3]

## ------------------------------------------------------------------------
t2 <- system.time(
gp2 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom)
)[3]

## ------------------------------------------------------------------------
t3 <- system.time(
gp3 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom,
             rotate = FALSE, trans = "BC")
)[3]
t4 <- system.time(
gp4 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom,
             trans = "BC")
)[3]

## ---- fig.show='hold'----------------------------------------------------
plot(gp1, ru_scale = FALSE, cex.main = 0.75, cex.lab = 0.75, 
  main = paste("no transformation \n pa = ", round(gp1$pa, 3), 
               ", time = ", round(t1, 2), "s"))
plot(gp2, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75, 
  main = paste("rotation \n pa = ", round(gp2$pa, 3),
               ", time = ", round(t2, 2), "s"))
plot(gp3, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75, 
  main = paste("Box-Cox \n pa = ", round(gp3$pa, 3),
               ", time = ", round(t3, 2), "s"))
plot(gp4, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75,
  main = paste("Box-Cox and rotation \n pa = ", round(gp4$pa, 3),
               ", time = ", round(t4, 2), "s"))

## ---- echo = FALSE-------------------------------------------------------
thresh <- quantile(gom, probs = 0.95)
fp <- set_prior(prior = "flat", model = "gp", min_xi = -1)
t2 <- system.time(
gp2 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom)
)[3]
t4 <- system.time(
gp4 <- rpost(n = n, model = "gp", prior = fp, thresh = thresh, data = gom,
             trans = "BC")
)[3]

## ---- fig.show='hold', echo = FALSE--------------------------------------
plot(gp2, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75, 
  main = paste("rotation \n pa = ", round(gp2$pa, 3),
               ", time = ", round(t2, 2), "s"))
plot(gp4, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75,
  main = paste("Box-Cox and rotation \n pa = ", round(gp4$pa, 3),
               ", time = ", round(t4, 2), "s"))

## ------------------------------------------------------------------------
u_prior_fn <- function(x, ab) {
  #
  # Calculates the the log of the (improper) prior density for GP parameters 
  # (sigma_u, xi) in which log(sigma_u) is uniform on the real line and xi has 
  # a beta(alpha, beta)-type prior on the interval (-1, 1).
  #
  # Args:
  #   x  : A numeric vector.  GP parameter vector (sigma, xi).
  #   ab : A numeric vector.  Hyperparameter vector (alpha, beta), where
  #        alpha and beta must be positive.
  #
  # Returns : the value of the log-prior at x.
  #
  if (x[1] <= 0 | x[2] <= -1 | x[2] >= 1) {
    return(-Inf)
  }  
  return(-log(x[1]) + (ab[1] - 1) * log(1 + x[2]) +
           (ab[2] - 1) * log(1 - x[2]))
}
up <- set_prior(prior = u_prior_fn, ab = c(2, 2), model = "gp")
gp_u <- rpost(n = n, model = "gp", prior = up, thresh = thresh, data = gom)

## ------------------------------------------------------------------------
data(portpirie)
mat <- diag(c(10000, 10000, 100))
pn <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)
p1 <- rpost(n = n, model = "gev", prior = pn, data = portpirie)
p1$pa

## ---- fig.width = 7------------------------------------------------------
plot(p1, cex.main = 0.75, cex.lab = 0.75, 
  main = "original scale")
plot(p1, ru_scale = TRUE, cex.main = 0.75, cex.lab = 0.75,
     main = "sampling scale")

## ------------------------------------------------------------------------
# evdbayes
t0 <- c(3.87, 0.2, -0.05) 
s <- c(.06, .25, .25)
b <- 200
p2 <- posterior(n + b - 1, t0, prior = pn, lh = "gev", data = portpirie, 
                psd = s, burn = b)

## ---- fig.width = 7------------------------------------------------------
par(mfrow = c(1,3))
plot(density(p2[, 1], adj = 2), main = "", xlab = expression(mu))
lines(density(p1$sim_vals[, 1], adj = 2), lty = 2)
plot(density(p2[, 2], adj = 2), main = "", xlab = expression(sigma))
lines(density(p1$sim_vals[, 2], adj = 2), lty = 2)
legend("topright", legend = c("evdbayes", "revdbayes"), lty = 1:2, cex = 0.8)
plot(density(p2[, 3], adj = 2), main = "", xlab = expression(xi))
lines(density(p1$sim_vals[, 3], adj = 2), lty = 2)

## ------------------------------------------------------------------------
u_gev_prior_fn <- function(x, ab) {
  #
  # Calculates the the log of the (improper) prior density for GEV parameters 
  # (mu, sigma, xi) in which mu and log(sigma) are each uniform on the real 
  # line and xi has a beta(alpha, beta)-type prior on the interval (-1, 1).
  #
  # Args:
  #   x  : A numeric vector.  GP parameter vector (mu, sigma, xi).
  #   ab : A numeric vector.  Hyperparameter vector (alpha, beta), where
  #        alpha and beta must be positive.
  #
  # Returns : the value of the log-prior at x.
  #
  if (x[2] <= 0 | x[3] <= -1 | x[3] >= 1) {
    return(-Inf)
  }  
  return(-log(x[2]) + (ab[1] - 1) * log(1 + x[3]) +
           (ab[2] - 1) * log(1 - x[3]))
}
up <- set_prior(prior = u_gev_prior_fn, ab = c(2, 2), model = "gev")
# Using revdbayes function rpost()
p1 <- rpost(n = n, model = "gev", prior = up, thresh = thresh, data = gom)
# Using evdbayes function posterior()
t0 <- c(3.87, 0.2, -0.05) 
s <- c(.06, .25, .25)
p2 <- posterior(n + 200, t0, prior = up, lh = "gev", data = portpirie, psd = s)

## ------------------------------------------------------------------------
data(oxford)
prox <- prior.prob(quant = c(85,88,95), alpha = c(4,2.5,2.25,0.25))
# evdbayes
t0 <- c(84,4.2,-0.3) 
s <- c(1.25,.2,.1) 
b <- 1000
ox.post <- posterior(n + b - 1, t0, prox, lh = "gev", data = oxford, psd = s, 
                     burn = b)
# revdbayes
ox_post <- rpost(n = 1000, model = "gev", prior = prox, data = oxford)

## ---- fig.width = 7------------------------------------------------------
par(mfrow = c(1,3))
plot(density(ox.post[, 1], adj = 2), main = "", xlab = expression(mu))
lines(density(ox_post$sim_vals[, 1], adj = 2), lty = 2)
plot(density(ox.post[, 2], adj = 2), main = "", xlab = expression(sigma))
lines(density(ox_post$sim_vals[, 2], adj = 2), lty = 2)
legend("topright", legend = c("evdbayes", "revdbayes"), lty = 1:2, cex = 0.8)
plot(density(ox.post[, 3], adj = 2), main = "", xlab = expression(xi))
lines(density(ox_post$sim_vals[, 3], adj = 2), lty = 2)

## ------------------------------------------------------------------------
data(rainfall)
rthresh <- 40
prrain <- prior.quant(shape = c(38.9,7.1,47), scale = c(1.5,6.3,2.6))

## ---- fig.width = 7------------------------------------------------------
# Annual maximum parameterisation (number of blocks = number of years of data)
rn0 <- rpost(n = n, model = "pp", prior = prrain, data = rainfall, 
  thresh = rthresh, noy = 54, rotate = FALSE)
plot(rn0)
rn0$pa
# Rotation about maximum a posteriori estimate (MAP)
system.time(
rn1 <- rpost(n = n, model = "pp", prior = prrain, data = rainfall, 
  thresh = rthresh, noy = 54)
)
plot(rn1, ru_scale = TRUE)
rn1$pa

## ---- fig.width = 7------------------------------------------------------
# Number of blocks = number of threshold excesses
n_exc <- sum(rainfall > rthresh, na.rm = TRUE)
rn2 <- rpost(n = n, model = "pp", prior = prrain, data = rainfall, 
  thresh = rthresh, noy = 54, use_noy = FALSE, rotate = FALSE)
rn2$pa
plot(rn2, ru_scale = TRUE)
# Number of blocks = number of threshold excesses, rotation about MAP
system.time(
rn3 <- rpost(n = n, model = "pp", prior = prrain, data = rainfall,
  thresh = rthresh, noy = 54, use_noy = FALSE)
)
plot(rn3, ru_scale = TRUE)
rn3$pa

## ------------------------------------------------------------------------
# evdbayes
t0 <- c(43.2, 7.64, 0.32) 
s <- c(2, .2, .07)
b <- 2000
rn.post <- posterior(n + b - 1, t0, prrain, "pp", data = rainfall, 
                     thresh = 40, noy = 54, psd = s, burn = b)

## ---- fig.width = 7------------------------------------------------------
par(mfrow = c(1,3))
plot(density(rn.post[, 1], adj = 2), main = "", xlab = expression(mu))
lines(density(rn1$sim_vals[, 1], adj = 2), lty = 2)
plot(density(rn.post[, 2], adj = 2), main = "", xlab = expression(sigma))
lines(density(rn1$sim_vals[, 2], adj = 2), lty = 2)
legend("topright", legend = c("evdbayes", "revdbayes"), lty = 1:2, cex = 0.8)
plot(density(rn.post[, 3], adj = 2), main = "", xlab = expression(xi))
lines(density(rn1$sim_vals[, 3], adj = 2), lty = 2)

## ---- fig.width = 7------------------------------------------------------
data(venice)
mat <- diag(c(10000, 10000, 100))
pv <- set_prior(prior = "norm", model = "gev", mean = c(0,0,0), cov = mat)
osv <- rpost(n = n, model = "os", prior = pv, data = venice)
plot(osv)

## ---- fig.width = 7------------------------------------------------------
plot(osv, ru_scale = TRUE)

