R/select.knots.R

In ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data: Goodness-of-fit test of covariance for sparse functional data

############################
# Written by: Stephanie Chen (stchen3@ncsu.edu)
# Purpose: Calculate correct knot locations for b-splines
# Updated: Aug 4, 2018

select.knots <- function(t, knots = 10, p = 3, option = "equally-spaced") {
  qs <- seq(0, 1, length = knots + 1)
  max_t <- max(t)
  min_t <- min(t)
  range_t <- max_t - min_t

  min_t <- min_t - range_t * 0.001 # follow Simon Wood's implementation
  max_t <- max_t + range_t * 0.001 #

  if (option == "equally-spaced") {
    knots <- (max_t - min_t) * qs + min_t
  }
  if (option == "quantile") {
    loc_max <- which.max(t)
    loc_min <- which.min(t)
    s <- t + rnorm(length(t)) * range_t / 100
    s[loc_max] <- t[loc_max]
    s[loc_min] <- t[loc_min]
    t <- s
    knots <- as.vector(quantile(t, qs))
  }

  K <- length(knots)
  knots_left <- 2 * knots[1] - knots[p:1 + 1]
  knots_right <- 2 * knots[K] - knots[K - (1:p)]

  if (p > 0) {
    return(c(knots_left, knots, knots_right))
  }
  if (p == 0) {
    return(knots)
  }
}