plot.sbss: Plot Method for an Object of Class 'sbss'

In SpatialBSS: Blind Source Separation for Multivariate Spatial Data

Plot Method for an Object of Class 'sbss'

Description

plot.sbss is an interface to the standard plot method for the class of the estimated source random field.

Usage

## S3 method for class 'sbss'
plot(x, which = 1:ncol(x$s), ...)

Arguments

x	object of class 'sbss'. Usually result of sbss.
which	a numeric vector indicating which components of the latent field should be plotted.
...	further arguments to the plot method of class(x$s), which is either spplot or plot.

Details

This method calls the corresponding plot method of class(x$s). Either spplot for class(x$s) is SpatialPointsDataFrame or plot.sf for class(x$s) is sf. If x$s is a matrix then it is internally cast to SpatialPointsDataFrame and spplot is used for plotting. Arguments to the corresponding plot functions can be given through ....

See Also

sbss, spplot, plot.sf

Examples

# simulate coordinates
coords <- runif(1000 * 2) * 20
dim(coords) <- c(1000, 2)
coords_df <- as.data.frame(coords)
names(coords_df) <- c("x", "y")
# simulate random field
if (!requireNamespace('gstat', quietly = TRUE)) {
  message('Please install the package gstat to run the example code.')
} else {
  library(gstat)
  model_1 <- gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = vgm(psill = 0.025, range = 1, model = 'Exp'), nmax = 20)
  model_2 <- gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = vgm(psill = 0.025, range = 1, kappa = 2, model = 'Mat'), 
                   nmax = 20)
  model_3 <- gstat(formula = z ~ 1, locations = ~ x + y, dummy = TRUE, beta = 0, 
                   model = 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 <- as.matrix(cbind(field_1, field_2, field_3))

  # compute ring kernel matrices
  kernel_parameters <- c(0, 1, 1, 2, 2, 3)
  ring_kernel_list <- spatial_kernel_matrix(coords, 'ring', kernel_parameters)
  
  # apply sbss SpatialPointsDataFrame object
  field_sp <- sp::SpatialPointsDataFrame(coords = coords, data = data.frame(field))
  res_sp <- sbss(field_sp, kernel_list = ring_kernel_list)
  
  # plot with SpatialPointsDataFrame object
  plot(res_sp)
  
  # plot with SpatialPointsDataFrame object 
  # and additional arguments for spplot function
  plot(res_sp, colorkey = TRUE, as.table = TRUE, cex = 1)

  # apply sbss with sf object
  if (!requireNamespace('sf', quietly = TRUE)) {
    message('Please install the package sf to run the example code.')
  } else {
    field_sf <- sf::st_as_sf(data.frame(coords = coords, field), 
                             coords = c(1,2))
    res_sf <- sbss(x = field_sf, kernel_list = ring_kernel_list)
    
    # plot with sf object
    plot(res_sf)
    
    # plot with sf object 
    # and additional arguments for plot.sf function
    plot(res_sf, axes = TRUE, key.pos = 4)
  }
}

SpatialBSS documentation built on July 26, 2023, 5:37 p.m.