x_1D <- as.matrix(seq(-5, 5, length = 10))
Phi_1D <- exp(-x_1D^2) / norm(exp(-x_1D^2), "F")
set.seed(1234)
Y_1D <- rnorm(n = 100, sd = 3) %*% t(Phi_1D) + matrix(rnorm(n = 100 * 10), 100, 10)
cv_1D <- spatpca(x = x_1D, Y = Y_1D, num_cores = 2)
plot(cv_1D)
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