For a categorical variable – income group, region, a yes/no – count the neighbouring pairs whose two countries fall in the same category, and compare each count with what random labelling would give. Many more same-category joins than chance is clustering; fewer is a checkerboard.
Arguments
- data
A country-level frame with
iso3c.- value
The categorical column (unquoted): a factor, character or logical.
- weights
A
country_weights()object;NULL(default) is k-nearest neighbours (k = 5). A pair is joined when either country lists the other as a neighbour.- n_perm
Permutations of the labels for the p-values (default
999).
Value
A tibble with one row per category: category, n (countries in
it), joins (pairs within it), expected and sd (under permutation),
z, and p_value (two-sided). Its "n_joins" attribute is the number
of joined pairs.
See also
morans_i() for a numeric variable
Examples
snap <- countryatlas::world_snapshot$countries
join_counts(snap, income, n_perm = 199)
#> # A tibble: 5 × 7
#> category n joins expected sd z p_value
#> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 High income 80 146 85.0 5.91 10.3 0.005
#> 2 Low income 26 33 8.77 2.57 9.42 0.005
#> 3 Lower middle income 54 65 38.3 5.42 4.93 0.005
#> 4 Not classified 1 0 0 0 NA 1
#> 5 Upper middle income 54 61 38.8 4.80 4.63 0.005