Description Usage Arguments Value References See Also Examples
ProjSpTi
produces samples from the posterior distribution of the spatial
projected normal model.
1 2 3 4 5 6 7 8 9 10 11 | ProjSpTi(x = x, coords = coords, times = c(), start = list(alpha =
c(1, 1, 0.5, 0.5), tau = c(0.1, 0.5), rho_sp = c(0.1, 0.5), rho_t =
c(0.1, 0.5), sep_par = c(0.1, 0.5), sigma2 = c(0.1, 0.5), r = sample(1,
length(x), replace = T)), priors = list(tau = c(8, 14), rho_sp = c(8,
14), rho_t = c(8, 14), sep_par = c(8, 14), sigma2 = c(), alpha_mu = c(1,
1), alpha_sigma = c()), sd_prop = list(sigma2 = 0.5, tau = 0.5, rho_sp
= 0.5, rho_t = 0.5, sep_par = 0.5, sdr = sample(0.05, length(x), replace
= T)), iter = 1000, BurninThin = c(burnin = 20, thin = 10),
accept_ratio = 0.234, adapt_param = c(start = 1, end = 1e+07, exp =
0.9, sdr_update_iter = 50), n_chains = 2, parallel = FALSE,
n_cores = 1)
|
x |
a vector of n circular data in [0,2π) If they are not in [0,2π), the function will tranform the data in the right interval |
coords |
an nx2 matrix with the sites coordinates |
times |
an n vector with the times of .... |
start |
a list of 4 elements giving initial values for the model parameters. Each elements is a vector with
|
priors |
a list of 7 elements to define priors for the model parameters:
|
sd_prop |
=list of 4 elements. To run the MCMC for the rho_sp, tau and sigma2 parameters and r vector we use an adaptive metropolis and in sd_prop we build a list of initial guesses for these three parameters and the r vector |
iter |
iter number of iterations |
BurninThin |
a vector of 2 elements with the burnin and the chain thinning |
accept_ratio |
it is the desired acceptance ratio in the adaptive metropolis |
adapt_param |
a vector of 4 elements giving the iteration number at which the adaptation must start and end. The third element (exp) must be a number in (0,1) is a parameter ruling the speed of changes in the adaptation algorithm, it is recommended to set it close to 1, if it is too small non positive definite matrices may be generated and the program crashes. The last element (sdr_update_iter) must be a positive integer defining every how many iterations there is the update of the sd (vector) of (vector) r. |
n_chains |
integer, the number of chains to be launched (default is 1, but we recommend to use at least 2 for model diagnostic) |
parallel |
logical, if the multiple chains must be lunched in parallel (you should install doParallel package). Default is FALSE |
n_cores |
integer, required if parallel=TRUE, the number of cores to be used in the implementation. Default value is 1. |
it returns a list of n_chains
lists each with elements
tau
, rho_sp
, rho_t
, sigma2
vectors with the thinned chains
alpha
a matrix with nrow=2
and ncol=
the length of thinned chains
r
a matrix with nrow=length(x)
and ncol=
the length of thinned chains
G. Mastrantonio, G.Jona Lasinio, A. E. Gelfand, "Spatio-temporal circular models with non-separable covariance structure", TEST 25 (2016), 331–350.
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
T. Gneiting, "Nonseparable, Stationary Covariance Functions for Space-Time Data", JASA 97 (2002), 590-600
ProjKrigSpTi
for spatio-temporal prediction under the spatio-temporal projected normal model,
WrapSpTi
to sample from the posterior distribution of the spatio-temporal
Wrapped Normal model and WrapKrigSpTi
for spatio-temporal prediction under the
same model
Other spatio-temporal models: WrapSpTi
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | 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))
# move to prediction once convergence is achieved using ProjKrigSpTi
|
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