Simulating spatial data

library("MASS")
library("dplyr")

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library("ggplot2")

set.seed(20171108)
sessionInfo()

## R version 3.4.1 (2017-06-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
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## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
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## [1] stats     graphics  grDevices utils     datasets  methods   base     
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<<<<<<< Updated upstream
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>>>>>>> Stashed changes

Grid <- expand.grid(x.easting = 1:10, x.northing = 1:10)
n <- nrow(Grid)

# Explanatory variables and coefficients
x1 <- rnorm(n) %>% round(2)
x2 <- rnorm(n) %>% round(2)

# Spatial field
distance <- as.matrix(dist(Grid))
omega <- MASS::mvrnorm(n     = 1, 
                       mu    = rep(0,n), 
                       Sigma = 0.4 * exp(-0.1 * distance))

eta <- x1 + x2 + omega

d <- Grid %>% 
  mutate(Y_normal = rnorm(n, eta, sd = 0.1) %>% round(2),
         Y_pois   = rpois(n, exp(eta)),
         trials   = 10,
         Y_binom  = rbinom(n = n, size = trials, prob = 1/(1+exp(-eta))),
         x1       = x1,
         x2       = x2)

ggplot(d %>% mutate(omega=omega), 
       aes(x=x.easting, y=x.northing)) +
  geom_raster(aes(fill = omega)) +
  theme_bw()

ggplot(d, aes(x = x.easting, y = x.northing, fill = Y_normal)) + 
  geom_raster() +
  theme_bw()

ggplot(d, aes(x = x.easting, y = x.northing, fill = Y_pois)) + 
  geom_raster() +
  theme_bw()

ggplot(d, aes(x = x.easting, y = x.northing, fill = Y_binom/trials)) + 
  geom_raster() +
  theme_bw()

save(d, file="data/spatial20171108.rda")