R/zzz.r

In sigmafelix/autoSTK: Automatic spatio-temporal kriging

## Misc for npsp integration: subject to retire

svariso <- function(input, vars, maxlag = 30000, nlags = 10, estimator = "modulus") {
  if (sum(grepl("Spatial.*", class(input))) != 0) {
    .svariso.sp(input, vars, maxlag, nlags, estimator)
  } else if (sum(grepl("sf.*", class(input))) != 0) {
    .svariso.sf(input, vars, maxlag, nlags, estimator)
  } else {
    stop("The input is not sf or sp-compatible dataset")
  }
}

.as.svariso.variogram <- function(vg, estimator = "modulus") {
  sv <- list(
    biny = vg$gamma,
    binw = vg$np,
    grid = npsp::grid.par(
      n = nrow(vg),
      min = min(vg$dist, na.rm = T),
      max = max(vg$dist, na.rm = T),
      lag = (vg$spacelag[2] - vg$spacelag[1])
    ),
    data = list(x = 2, y = 1, med = weighted.mean(vg$gamma, vg$np)),
    svar = list(
      type = "isotropic",
      estimator = estimator
    )
  )
  attr(sv, "class") <- c("svar.bin", "bin.data", "bin.den", "data.grid")
  return(sv)
}

.svariso.sp <- function(Sp, vars, ml, nlags, estimator = "modulus") {
  coord.n <- coordinates(Sp)

  y <- Sp@data[, vars]
  ssp <- svar.bin(coord.n, y, estimator = estimator, maxlag = ml, nlags = nlags)
  return(ssp)
}

# sf implementation
.svariso.sf <- function(sf, vars, ml, nlags, estimator = "modulus") {
  coord.n <- st_coordinates(sf)
  y <- st_set_geometry(sf, NULL)[, vars] %>% unlist()
  ssp <- svar.bin(coord.n, y, estimator = estimator, maxlag = ml, nlags = nlags)
  return(ssp)
}