Capture the Dominant Spatial Pattern with Two-Dimensional Locations

Wen-Ting Wang

Objective

Represent how to use SpatPCA for two-dimensional data for capturing the most dominant spatial pattern

Basic settings

Used packages

library(SpatPCA)
library(ggplot2)

base_theme <- theme_minimal(base_size = 10, base_family = "Times") +
  theme(legend.position = "bottom")
fill_bar <- guides(fill = guide_colourbar(
    barwidth = 10,
    barheight = 0.5,
    label.position = "bottom")
  )
coltab <- with(
  list(),
  colorRampPalette(c("#3b4cc0", "#f7f7f7", "#b40426"))(128)
)
color_scale_limit <- c(-.8, .8)

Selected realizations are displayed as static images below. #### True spatial pattern (eigenfunction) - The underlying spatial pattern below indicates realizations will vary dramatically at the center and be almost unchanged at the both ends of the curve.

set.seed(1024)
p <- 25
n <- 8
location <-
  matrix(rep(seq(-5, 5, length = p), 2), nrow = p, ncol = 2)
expanded_location <- expand.grid(location[, 1], location[, 2])
unnormalized_eigen_fn <-
  as.vector(exp(-location[, 1] ^ 2) %*% t(exp(-location[, 2] ^ 2)))
true_eigen_fn <-
  unnormalized_eigen_fn / norm(t(unnormalized_eigen_fn), "F")

plot_df <- data.frame(
  location_dim1 = expanded_location[, 1],
  location_dim2 = expanded_location[, 2],
  eigenfunction = true_eigen_fn
)

ggplot(plot_df, aes(location_dim1, location_dim2)) +
  geom_tile(aes(fill = eigenfunction)) +
  scale_fill_gradientn(colours = coltab, limits = color_scale_limit) +
  base_theme +
  labs(title = "True Eigenfunction", fill = "") +
  fill_bar

Experiment

Generate 2-D realizations

realizations <- rnorm(n = n, sd = 3) %*% t(true_eigen_fn) + matrix(rnorm(n = n * p^2), n, p^2)

Animate realizations

realization_df <- data.frame(
  location_dim1 = expanded_location[, 1],
  location_dim2 = expanded_location[, 2],
  value = realizations[1, ]
)

ggplot(realization_df, aes(location_dim1, location_dim2)) +
  geom_tile(aes(fill = value)) +
  scale_fill_gradientn(colours = coltab, limits = c(-10, 10)) +
  base_theme +
  labs(title = "1st realization", fill = "") +
  fill_bar

Apply SpatPCA::spatpca

We add a candidate set of tau2 to see how SpatPCA obtain a localized smooth pattern.

tau2 <- c(0, exp(seq(log(10), log(400), length = 10)))
cv <- spatpca(x = expanded_location, Y = realizations, tau2 = tau2)
eigen_est <- cv$eigenfn

Compare SpatPCA with PCA

The following figure shows that SpatPCA can find sparser pattern than PCA, which is close to the true pattern.

plot_df <- data.frame(
  location_dim1 = expanded_location[, 1],
  location_dim2 = expanded_location[, 2],
  spatpca = eigen_est[, 1],
  pca = svd(realizations)$v[, 1]
)

plot_df_long <- rbind(
  data.frame(location_dim1 = plot_df$location_dim1,
             location_dim2 = plot_df$location_dim2,
             estimate = "spatpca",
             eigenfunction = plot_df$spatpca),
  data.frame(location_dim1 = plot_df$location_dim1,
             location_dim2 = plot_df$location_dim2,
             estimate = "pca",
             eigenfunction = plot_df$pca)
)

ggplot(plot_df_long, aes(location_dim1, location_dim2)) +
  geom_tile(aes(fill = eigenfunction)) +
  scale_fill_gradientn(colours = coltab, limits = color_scale_limit) +
  base_theme +
  facet_wrap(~estimate) +
  labs(fill = "") +
  fill_bar