Description Usage Arguments Details Value See Also Examples

`predict.sbss`

predicts the estimated source random field on a grid with Inverse Distance Weighting (IDW) and plots these predictions.

1 2 |

`object` |
object of class |

`p` |
numeric. The positive power parameter for IDW. Default is 2. |

`n_grid` |
numeric. Each dimension of the spatial domain is divided by this integer to derive a grid for IDW predictions. Default is 50. |

`which` |
a numeric vector indicating which components of the latent field should be predicted. |

`...` |
further arguments to the plot method of |

IDW predictions are made on a grid. The side lengths of the rectangular shaped grid cells are derived by the differences of the rounded maximum and minimum values divided by the `n_grid`

argument for each column of `object$coords`

. Hence, the grid contains a total of `n_grid ^ 2`

points. The power parameter of the IDW predictions is given by `p`

(default: 2).

The predictions are plotted with 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 `...`

as it is done by the method `plot.sbss`

.

The return is dependent on the class of the latent field in the `'sbss'`

object.
If `class(object$s)`

is a matrix then a list with the following entries is returned:

`vals_pred_idw` |
a matrix of dimension |

`coords_pred_idw ` |
a matrix of dimension |

If `class(object$s)`

is `SpatialPointsDataFrame`

or `sf`

then the predicted values and their coordinates are returned as an object of the corresponding class.

The return is invisible.

`sbss`

, `plot.sbss`

, `spplot`

, `plot.sf`

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 | ```
# simulate coordinates
coords <- runif(1000 * 2) * 20
dim(coords) <- c(1000, 2)
# simulate random field
if (!requireNamespace('RandomFields', quietly = TRUE)) {
stop('Please install the package RandomFields to run the example code.')
} else {
RandomFields::RFoptions(spConform = FALSE)
field_1 <- RandomFields::RFsimulate(model = RandomFields::RMexp(),
x = coords)
field_2 <- RandomFields::RFsimulate(model = RandomFields::RMspheric(),
x = coords)
field_3 <- RandomFields::RFsimulate(model = RandomFields::RMwhittle(nu = 2),
x = coords)
field <- cbind(field_1, field_2, field_3)
}
# apply sbss with three ring kernels
kernel_borders <- c(0, 1, 1, 2, 2, 4)
res_sbss <- sbss(field, coords, 'ring', kernel_borders)
# predict latent fields on grid with default settings
predict(res_sbss)
# predict latent fields on grid with custom plotting settings
predict(res_sbss, colorkey = TRUE, as.table = TRUE, cex = 1)
# predict latent fields on a 60x60 grid
predict(res_sbss, n_grid = 60, colorkey = TRUE, as.table = TRUE, cex = 1)
# predict latent fields with a higher IDW power parameter
predict(res_sbss, p = 10, colorkey = TRUE, as.table = TRUE, cex = 1)
# predict latent fields and save the predictions
predict_list <- predict(res_sbss, p = 5, colorkey = TRUE, as.table = TRUE, cex = 1)
``` |

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