Local Neighbor Match Test — nb_match

Implements the Local Neighbor Match Test as described in Tobler's Law in a Multivariate World (Anselin and Li, 2020).

Usage

nb_match_test(
  x,
  nb,
  wt = st_weights(nb),
  k = 10,
  nsim = 499,
  scale = TRUE,
  .method = "euclidian",
  .p = 2
)

Arguments

x: a numeric vector or a list of numeric vectors of equal length.
nb: a neighbor list object for example as created by st_contiguity().
wt: a weights list as created by st_weights().
k: the number of neighbors to identify in attribute space. Should be the same as number of neighbors provided in st_knn.
nsim: the number of simulations to run for calculating the simulated p-value.
scale: default TRUE. Whether x should be scaled or not. Note that measures should be standardized.
.method: default "euclidian". The distance measure passed to stats::dist().
.p: default 2. The power of Minkowski distance passed to the p argument in stats::dist().

Value

a data.frame with columns

n_shared (integer): the number of shared neighbors between geographic and attribute space
nb_matches (list): matched neighbor indexes. Each element is an integer vector of same length as the ith observation of n_shared
knn_nb (list): the neighbors in attribute space
probability (numeric): the geometric probability of observing the number of matches
p_sim (numeric): a folded simulated p-value

Examples

library(magrittr)
guerry %>%
  dplyr::transmute(nb = st_knn(geometry, k = 10),
         nmt = nb_match_test(list(crime_pers, literacy, suicides),
                             nb, nsim = 999)) %>%
  tidyr::unnest(nmt)
#> ! Polygon provided. Using point on surface.
#> Simple feature collection with 85 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
#> CRS:           NA
#> # A tibble: 85 × 7
#>    nb         n_shared nb_matches knn_nb proba…¹ p_sim                  geometry
#>    <nb>          <int> <list>     <list>   <dbl> <dbl>            <MULTIPOLYGON>
#>  1 <int [10]>        1 <int [1]>  <int>  0.400   0.662 (((801150 2092615, 80066…
#>  2 <int [10]>        3 <int [3]>  <int>  0.0782  0.087 (((729326 2521619, 72932…
#>  3 <int [10]>        5 <int [5]>  <int>  0.00147 0.001 (((710830 2137350, 71174…
#>  4 <int [10]>        4 <int [4]>  <int>  0.0141  0.015 (((882701 1920024, 88240…
#>  5 <int [10]>        0 <int [0]>  <int>  0.260   0.262 (((886504 1922890, 88573…
#>  6 <int [10]>        0 <int [0]>  <int>  0.260   0.264 (((747008 1925789, 74663…
#>  7 <int [10]>        5 <int [5]>  <int>  0.00147 0.003 (((818893 2514767, 81861…
#>  8 <int [10]>        5 <int [5]>  <int>  0.00147 0.004 (((509103 1747787, 50882…
#>  9 <int [10]>        3 <int [3]>  <int>  0.0782  0.085 (((775400 2345600, 77506…
#> 10 <int [10]>        4 <int [4]>  <int>  0.0141  0.016 (((626230 1810121, 62626…
#> # … with 75 more rows, and abbreviated variable name ¹probability
#> # ℹ Use `print(n = ...)` to see more rows