ProjSp() R function from [CircSpaceTime]

Samples from the Projected Normal spatial model

ProjSp produces samples from the posterior distribtion of the spatial projected normal model.


ProjSp(x = x, coords = coords, start = list(alpha = c(1, 1, 0.5,
  0.5), tau = c(0.1, 0.5), rho = c(0.1, 0.5), sigma2 = c(0.1, 0.5), r =
  rep(1, times = length(x))), priors = list(tau = c(8, 14), rho = c(8,
  14), sigma2 = c(), alpha_mu = c(1, 1), alpha_sigma = c()),
  sd_prop = list(sigma2 = 0.5, tau = 0.5, rho = 0.5, sdr = sample(0.05,
  length(x), replace = TRUE)), 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), corr_fun = "exponential",
  kappa_matern = 0.5, n_chains = 2, parallel = FALSE, n_cores = 1)

Arguments

x: a vector of n circular data in $[0,2\pi)$ . If they are not in $[0,2\pi)$ , the function will transform the data in the right interval
coords: an nx2 matrix with the sites coordinates
start: a list of 4 elements giving initial values for the model parameters. Each elements is a vector with n_chains elements
- alpha the 2-d vector of the means of the Gaussian bi-variate distribution,
- tau the correlation of the two components of the linear representation,
- rho the spatial decay parameter,
- sigma2 the process variance,
- r the vector of length(x), latent variable
priors: a list of 4 elements to define priors for the model parameters:
- alpha_mu: a vector of 2 elements, the means of the bivariate Gaussian distribution,
- alpha_sigma: a 2x2 matrix, the covariance matrix of the bivariate Gaussian distribution,
- rho: vector of 2 elements defining the minimum and maximum of a uniform distribution,
- tau: vector of 2 elements defining the minimum and maximum of a uniform distribution, with the constraints min(tau) >= -1 and max(tau) <= 1
- sigma2: a vector of 2 elements defining the shape and rate of an inverse-gamma distribution,
sd_prop: list of 4 elements. To run the MCMC for the rho, 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: 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.
corr_fun: characters, the name of the correlation function; currently implemented functions are c("exponential", "matern","gaussian")
kappa_matern: numeric, the smoothness parameter of the Matern correlation function, default is kappa_matern = 0.5 (the exponential function)
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.

Returns

it returns a list of n_chains lists each with elements

rho,tau, 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
corr_fun: characters with the type of spatial correlation chosen
distribution: characters, always "ProjSp"

Examples


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 using 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
tau     <- 0.2
sigma2  <- 1
alpha   <- c(0.5,0.5)
SIGMA   <- sigma2*exp(-rho*Dist)

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 to have values in (0,2pi)

hist(theta)
rose_diag(theta)

val <- sample(1:n,round(n*0.1))

################some useful quantities
rho.min <- 3/max(Dist)
rho.max <- rho.min+0.5

set.seed(100)

mod <- ProjSp(
 x       = theta[-val],
 coords    = coords[-val,],
 start   = list("alpha"      = c(0.92, 0.18, 0.56, -0.35),
                "rho"     = c(0.51,0.15),
                "tau"     = c(0.46, 0.66),
                "sigma2"    = c(0.27, 0.3),
                "r"       = abs(rnorm(  length(theta))  )),
 priors   = list("rho"      = c(rho.min,rho.max),
                 "tau"      = c(-1,1),
                 "sigma2"    = c(10,3),
                 "alpha_mu" = c(0, 0),
                 "alpha_sigma" = diag(10,2)
 )  ,
 sd_prop   = list("sigma2" = 0.1, "tau" = 0.1, "rho" = 0.1,
                  "sdr" = sample(.05,length(theta), replace = TRUE)),
 iter    = 10000,
 BurninThin    = c(burnin = 7000, thin = 10),
 accept_ratio = 0.234,
 adapt_param = c(start = 130000, end = 120000, exp = 0.5),#no adaptation
 corr_fun = "exponential",
 kappa_matern = .5,
 n_chains = 2 ,
 parallel = TRUE ,
 n_cores = 2
)
# If you don't want to install/use DoParallel
# please set parallel = FALSE. Keep in mind that it can be substantially slower
# How much it takes?

check <-  ConvCheck(mod)
check$Rhat #close to 1 we have convergence

#### graphical check
par(mfrow=c(3,2))
coda::traceplot(check$mcmc)

par(mfrow=c(1,1))
# once convergence is achieved move to prediction using ProjKrigSp

References

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

ProjSp function

Samples from the Projected Normal spatial model

Arguments

Returns

Examples

References

See Also