Description Usage Arguments Value References See Also Examples
CRPScirc
function computes the The Continuous Ranked Probability Score for Circular Variables
1 |
obs, |
a vector of the values of the process at the test locations |
sim, |
a matrix with nrow the test locations and ncol the number of posterior samples from the posterior distributions |
bycol, |
logical. It is TRUE if the columns of sim represent the observations and the rows the posterior samples, the default value is FALSE |
a list of 2 elements
CRPSvec
a vector of CRPS, one element for each test point
CRPS
the overall mean
Grimit, Eric P., Tilmann Gneiting, Veronica J. Berrocal, Nicholas Alexander Johnson. "The Continuous Ranked Probability Score for Circular Variables and its Application to Mesoscale Forecast Ensemble Verification", Q.J.R. Meteorol. Soc. 132 (2005), 2925-2942.
ProjKrigSp
and WrapKrigSp
for posterior spatial interpolation, and
ProjKrigSpTi
and WrapKrigSpTi
for posterior spatio-temporal interpolation
Other model performance indices: APEcirc
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | #' library(CircSpaceTime)
## auxiliary function
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 with exponential spatial covariance function
####
set.seed(1)
n <- 20
coords <- cbind(runif(n,0,100), runif(n,0,100))
Dist <- as.matrix(dist(coords))
rho <- 0.05
sigma2 <- 0.3
alpha <- c(0.5)
SIGMA <- sigma2*exp(-rho*Dist)
Y <- rmnorm(1,rep(alpha,times=n), SIGMA)
theta <- c()
for(i in 1:n) {
theta[i] <- Y[i]%%(2*pi)
}
rose_diag(theta)
#validation set
val <- sample(1:n,round(n*0.1))
set.seed(12345)
mod <- WrapSp(
x = theta[-val],
coords = coords[-val,],
start = list("alpha" = c(.36,0.38),
"rho" = c(0.041,0.052),
"sigma2" = c(0.24,0.32),
"k" = rep(0,(n - length(val)))),
priors = list("rho" = c(0.04,0.08), #few observations require to be more informative
"sigma2" = c(2,1),
"alpha" = c(0,10)
),
sd_prop = list( "sigma2" = 0.1, "rho" = 0.1),
iter = 1000,
BurninThin = c(burnin = 500, thin = 5),
accept_ratio = 0.234,
adapt_param = c(start = 40000, end = 45000, exp = 0.5),
corr_fun = "exponential",
kappa_matern = .5,
parallel = FALSE,
#With doParallel, bigger iter (normally around 1e6) and n_cores>=2 it is a lot faster
n_chains = 2 ,
n_cores = 1
)
check <- ConvCheck(mod)
check$Rhat ## close to 1 means convergence has been reached
## graphical check
par(mfrow = c(3,1))
coda::traceplot(check$mcmc)
par(mfrow = c(1,1))
##### We move to the spatial interpolation
Krig <- WrapKrigSp(
WrapSp_out = mod,
coords_obs = coords[-val,],
coords_nobs = coords[val,],
x_obs = theta[-val]
)
#### check the quality of the prediction using APE and CRPS
ApeCheck <- APEcirc(theta[val],Krig$Prev_out)
CrpsCheck <- CRPScirc(theta[val],Krig$Prev_out)
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