## Not run: 
               ###########################################
               ##  SECTION 4: UNCONDITIONAL SIMULATION  ##
               ###########################################

RFoptions(seed = 0, height = 4)
## seed=0:  *ANY* simulation will have the random seed 0; set
##          RFoptions(seed=NA) to make them all random again
## height : height of X11
## always_close_device=FALSE: the pictures are kept on the screen


## Fig. 1: linear model of coregionalization
M1 <- c(0.9, 0.6)
M2 <- c(sqrt(0.19), 0.8)
model <- RMmatrix(M = M1, RMwhittle(nu = 0.3)) + 
         RMmatrix(M = M2, RMwhittle(nu = 2))
x <- y <- seq(-10, 10,  0.2)
simu <- RFsimulate(model, x, y)
plot(simu)


## Fig. 2: Wackernagel's delay model
model <- RMdelay(RMstable(alpha = 1.9, scale = 2), s = c(4, 4))
simu <- RFsimulate(model, x, y)
plot(simu, zlim = 'joint')


## Fig. 3: extended Wackernagel's delay model
model <- RMdelay(RMstable(alpha = 1.9, scale = 2), s = c(0, 4)) + 
         RMdelay(RMstable(alpha = 1.9, scale = 2), s = c(4, 0))
simu <- RFsimulate(model, x, y)
plot(simu, zlim = 'joint')
plot(model, dim = 2, xlim = c(-5, 5), main = "Covariance function", 
     cex = 1.5, col = "brown")


## Fig. 4: latent dimension model
## MARGIN.slices has the effect of choosing the third dimension
##               as latent dimension
## n.slices has the effect of choosing a bivariate model
model <- RMgencauchy(alpha = 1.5, beta = 3)
simu <- RFsimulate(model, x, y, z = c(0,1))
plot(simu, MARGIN.slices = 3, n.slices = 2)


## Fig. 5: Gneiting's bivariate Whittle-Matern model
model <- RMbiwm(nudiag = c(1, 2), nured = 1, rhored = 1, cdiag = c(1, 5), 
                s = c(1, 1, 2))
simu <- RFsimulate(model, x, y)
plot(simu)


## Fig. 6: anisotropic linear model of coregionalization
M1 <- c(0.9, 0.6)
M2 <- c(sqrt(0.19), 0.8)
A1 <- RMangle(angle = pi/4, diag = c(0.1, 0.5))
A2 <- RMangle(angle = 0, diag = c(0.1, 0.5))
model <- RMmatrix(M = M1, RMgengneiting(kappa = 0, mu = 2, Aniso = A1)) +
         RMmatrix(M = M2, RMgengneiting(kappa = 3, mu = 2, Aniso = A2))
simu <- RFsimulate(model, x, y)
plot(simu)


## Fig. 7: random vector field whose paths are curl free
## A 4-variate field is simulated, where the first component
## refers to the potential field, the second and third component
## to the curl free vector field and the forth component to the
## field of sinks and sources
model <- RMcurlfree(RMmatern(nu = 5), scale = 4)
simu <- RFsimulate(model, x, y)
plot(simu, select.variables = list(1, 2 : 3, 4))
plot(model, dim = 2, xlim = c(-3, 3), main = "", cex = 2.3, col="brown") 



## Fig. 8: Kolmogorov's model of turbulence
## See the physical literature for its derivation and details
x <- y <- seq(-2, 2, len = 20)
model <- RMkolmogorov()
simu <- RFsimulate(model, x, y, z = 1)
plot(simu, select.variables = list(1 : 2), col = c("red"))
plot(model, dim = 3, xlim = c(-3, 3), MARGIN = 1 : 2, cex = 2.3,
     fixed.MARGIN = 1.0, main = "", col = "brown")




               ###########################################
               ## SECTION 5: DATA ANALYSIS              ##
               ###########################################

## get the data     
data("weather")
PT <- weather[ , 1 : 2]  ## full data set takes more than 
##                                     half an hour to be analysed
## transformation of earth coordinates to Euclidean coordinates
Dist.mat <- as.vector(RFearth2dist(weather[ , 3 : 4]))
All <- TRUE
\dontshow{if(RFoptions()$internal$examples_reduced){warning("reduced data set")
All <- 1:10
PT <- weather[All , 1 : 2] 
Dist.mat <- as.vector(RFearth2dist(weather[All , 3 : 4]))
}}



#################################
## model definition            ##
#################################
## bivariate pure nugget effect:
nug <- RMmatrix(M = matrix(nc = 2, c(NA, 0, 0, NA)), RMnugget())
## parsimonious bivariate Matern model
pars.model <- nug + RMbiwm(nudiag = c(NA, NA), scale = NA, cdiag = c(NA, NA),
                           rhored = NA)
## whole bivariate Matern model
whole.model <- nug + RMbiwm(nudiag = c(NA, NA), nured = NA, s = rep(NA, 3),
                            cdiag = c(NA, NA), rhored = NA)



#################################
## model fitting and testing   ## 
#################################
## 'parsimonious model'
## fitting takes a while ( > 10 min)
pars <- RFfit(pars.model, distances = Dist.mat, dim = 3, data = PT)
print(pars)
print(pars, full=TRUE)
RFratiotest(pars)
#RFcrossvalidate(pars, x = weather[All , 3 : 4], data = PT, full = TRUE)

## 'whole model'
## fitting takes a while ( > 10 min)
whole <- RFfit(whole.model, distances = Dist.mat, dim = 3, data = PT)
print(whole, full=TRUE)
RFratiotest(whole)
#RFcrossvalidate(whole, x = weather[All , 3 : 4], data = PT, full = TRUE)

## compare parsimonious and whole
RFratiotest(nullmodel = pars, alternative = whole)


#################################
## kriging                     ##
#################################
## First, the coordinates are projected on a plane
a <- colMeans(weather[All , 3 : 4]) * pi / 180
P <- cbind(c(-sin(a[1]), cos(a[1]), 0),
           c(-cos(a[1]) * sin(a[2]), -sin(a[1]) * sin(a[2]), cos(a[2])),
           c(cos(a[1]) * cos(a[2]), sin(a[1]) * cos(a[2]), sin(a[2])))
x <- RFearth2cartesian(weather[All , 3 : 4])
plane <- (t(x) %*%P)[ , 1 : 2]

## here, kriging is performed on a rectangular that covers
## the projected points above. The rectangular is approximated
## by a grid of length 200 in each direction.
n <- 200 
r <- apply(plane, 2, range)
dta <- cbind(plane, weather[All , 1 : 2])
z <- RFinterpolate(pars, data = dta, dim = 2,
                   x = seq(r[1, 1], r[2, 1], length = n),
                   y = seq(r[1, 2], r[2, 2], length = n),
                   varunits = c("Pa", "K"), spConform = TRUE)
plot(z)

## End(Not run)