Nothing
#####################################################################
## BAU-level data and predictions with the meuse dataset
#####################################################################
library("FRK")
library("sp")
library("ggplot2")
library("gridExtra")
## Initialise seed to ensure reproducibility
set.seed(1)
## load the meuse data and convert to sp object
data("meuse")
coordinates(meuse) = ~ x + y
## load meuse.grid and convert to SpatialPixelsDataFrame
data("meuse.grid")
coordinates(meuse.grid) = ~ x + y
gridded(meuse.grid) = TRUE
## Remove any common fields in the data and the BAUs (in FRK all covariates
## need to be with the BAUs)
meuse$soil <- meuse$dist <- meuse$ffreq <- NULL
## formula for SRE
f <- log(zinc) ~ 1 + sqrt(dist)
## Run FRK
S <- FRK(f = f, # formula
data = list(meuse), # data (just meuse)
BAUs = meuse.grid, # the BAUs
regular = 0) # irregularly placed basis functions
## Predict at all the BAUs
Pred <- predict(S, obs_fs = FALSE) # Case 2 in paper (default)
## Plot results
BAUs_df <- as.data.frame(Pred) # convert the BAUs to data frame
gFRK_pred <- ggplot(BAUs_df) + geom_tile(aes(x,y,fill = mu)) +
scale_fill_distiller(palette = "Spectral") +
xlab("Easting (m)") + ylab("Northing (m)") + theme_bw()
gFRK_se <-ggplot(BAUs_df) + geom_tile(aes(x,y,fill = sd)) +
scale_fill_distiller(palette = "BrBG") +
xlab("Easting (m)") + ylab("Northing (m)") + theme_bw()
grid.arrange(gFRK_pred,gFRK_se,nrow=1)
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