Local Neighbor Match Test

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().

Examples

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 centroid.
#> Loading required namespace: dbscan
#> Simple feature collection with 85 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -5.139026 ymin: 42.33349 xmax: 8.23032 ymax: 51.08939
#> Geodetic CRS:  WGS 84
#> # A tibble: 85 × 7
#>    nb     n_shared nb_matches knn_nb probability p_sim                  geometry
#>    <nb>      <int> <list>     <list>       <dbl> <dbl>        <MULTIPOLYGON [°]>
#>  1 <int …        1 <int [1]>  <int …     0.400   0.651 (((4.92452 45.80404, 4.9…
#>  2 <int …        4 <int [4]>  <int …     0.0141  0.022 (((4.126445 49.67821, 4.…
#>  3 <int …        4 <int [4]>  <int …     0.0141  0.015 (((3.773349 46.22719, 3.…
#>  4 <int …        4 <int [4]>  <int …     0.0141  0.022 (((5.872688 44.22421, 5.…
#>  5 <int …        0 <int [0]>  <int …     0.260   0.276 (((5.921825 44.24841, 5.…
#>  6 <int …        0 <int [0]>  <int …     0.260   0.271 (((4.177986 44.31775, 4.…
#>  7 <int …        5 <int [5]>  <int …     0.00147 0.001 (((5.361486 49.59208, 5.…
#>  8 <int …        5 <int [5]>  <int …     0.00147 0.004 (((1.229289 42.72774, 1.…
#>  9 <int …        3 <int [3]>  <int …     0.0782  0.073 (((4.690867 48.08597, 4.…
#> 10 <int …        4 <int [4]>  <int …     0.0141  0.013 (((2.659241 43.29298, 2.…
#> # … with 75 more rows