## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----sim_coords---------------------------------------------------------------
coords <- runif(1000 * 2) * 20
dim(coords) <- c(1000, 2)
coords_df <- as.data.frame(coords)
names(coords_df) <- c("x", "y")

## ----sim_field----------------------------------------------------------------
if (requireNamespace("gstat", quietly = TRUE)) {
  mix_mat <- matrix(rnorm(9), 3, 3)
  
  model_1 <- gstat::gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = gstat::vgm(psill = 0.025, range = 1, model = 'Exp'), nmax = 20)
  model_2 <- gstat::gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = gstat::vgm(psill = 0.025, range = 1, kappa = 2, model = 'Mat'), 
                   nmax = 20)
  model_3 <- gstat::gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = gstat::vgm(psill = 0.025, range = 1, model = 'Gau'), nmax = 20)
  field_1 <- predict(model_1, newdata = coords_df, nsim = 1)$sim1
  field_2 <- predict(model_2, newdata = coords_df, nsim = 1)$sim1
  field_3 <- predict(model_3, newdata = coords_df, nsim = 1)$sim1

  field <- tcrossprod(cbind(field_1, field_2, field_3), mix_mat)
} else {
  message('The package gstat is needed to run this example.')
  field <- rnorm(nrow(coords) * 3)
  dim(field) <- c(nrow(coords), 3)
}

## -----------------------------------------------------------------------------
kernel_type <- 'ring'
kernel_parameters <- c(0, 1.5, 1.5, 3, 3, 4.5, 4.5, 6)

## ----sbss_func----------------------------------------------------------------
library('SpatialBSS')
sbss_res <- sbss(x = field, coords = coords, 
                 kernel_type = kernel_type, 
                 kernel_parameters = kernel_parameters)

## ----sbss_plot----------------------------------------------------------------
plot(sbss_res, colorkey = TRUE, as.table = TRUE, cex = 1)

## ----sbss_predict-------------------------------------------------------------
predict(sbss_res, p = 2, n_grid = 50, colorkey = TRUE, as.table = TRUE, cex = 1)

## ----sbss_sp------------------------------------------------------------------
field_sp <- sp::SpatialPointsDataFrame(coords = coords, data = data.frame(field))
res_sbss_sp <- sbss(x = field_sp, kernel_type = kernel_type, 
                    kernel_parameters = kernel_parameters)

## ----sbss_sf------------------------------------------------------------------
if (requireNamespace('sf', quietly = TRUE)) {
  field_sf <- sf::st_as_sf(data.frame(coords = coords, field), 
                           coords = c(1,2))
  res_sbss_sf <- sbss(x = field_sf, kernel_type = kernel_type,
                      kernel_parameters = kernel_parameters)
} else {
  message('Please install the package sf to run the example code.')
}


## ----sbss_func_ldiff----------------------------------------------------------
sbss_res_lcov <- sbss(x = field, coords = coords, 
                 kernel_type = kernel_type, lcov = 'ldiff',
                 kernel_parameters = kernel_parameters)

## ----sbss_func_ldiff_rob------------------------------------------------------
sbss_res_lcov <- sbss(x = field, coords = coords, rob_whitening = TRUE,
                 kernel_type = kernel_type, lcov = 'ldiff',
                 kernel_parameters = kernel_parameters)

## ----k_mat--------------------------------------------------------------------
ring_kernel_matrices <- spatial_kernel_matrix(coords, kernel_type, kernel_parameters)

## ----sbss_k_list--------------------------------------------------------------
sbss_k <- sbss(x = field, kernel_list = ring_kernel_matrices)

## ----lcov_mat-----------------------------------------------------------------
local_cov <- local_covariance_matrix(field, kernel_list = ring_kernel_matrices, 
                                     center = TRUE)

## ----ldiff_mat----------------------------------------------------------------
local_diff <- local_covariance_matrix(field, kernel_list =  ring_kernel_matrices, 
                                      lcov = 'ldiff', center = TRUE)

## ----anis---------------------------------------------------------------------
library('SpatialBSS')

data(veneto_weather)

kernel_type <- 'ring'
kernel_parameters <-c(0, 40000, 40000, 65000, 65000, 80000)
                                          
angles_1 <- list(c(0, pi / 8), c(pi / 4, pi / 8), c(pi / 2, pi / 8), c(3 * pi / 4, pi / 8))

sbss_anis <- sbss(x = veneto_weather, kernel_type = kernel_type, 
                  kernel_parameters = kernel_parameters, 
                  angles = angles_1)


