The local Moran statistic for two variables (Anselin, Syabri & Smirnov
2002): each country's standardised x against the average standardised
y of its neighbours, so "rich countries surrounded by long-lived
neighbours" is a cluster, and a rich country among short-lived neighbours
an outlier.
Usage
bivariate_lisa(
data,
x,
y,
weights = NULL,
n_perm = 9999,
alpha = 0.05,
p_adjust = c("fdr", "bonferroni", "holm", "none")
)Arguments
- data
A country-level frame with
iso3c.- x, y
The two value columns (unquoted).
- weights, n_perm, alpha, p_adjust
As in
local_morans().
Value
A tibble with one row per country used: iso3c, x, lag_y (the
neighbours' average y), ii, p_value, p_adjusted and cluster
("High-High", "Low-Low", "High-Low", "Low-High" or
"Not significant"), where the first word is the country's x and the
second its neighbours' y. A country needs both values to take part.
References
Anselin, L., Syabri, I. & Smirnov, O. (2002). Visualizing multivariate spatial correlation with dynamically linked windows. Proceedings, CSISS Workshop on New Tools for Spatial Data Analysis.
Examples
# \donttest{
snap <- countryatlas::world_snapshot$countries
bivariate_lisa(snap, gdp_per_capita, life_expectancy, n_perm = 499)
#> # A tibble: 199 × 7
#> iso3c x lag_y ii p_value p_adjusted cluster
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 ABW 33939. 76.4 0.217 0.482 0.689 Not significant
#> 2 AFG 374. 71.0 0.254 0.4 0.615 Not significant
#> 3 AGO 2799. 66.3 0.581 0.018 0.0731 Not significant
#> 4 ALB 6549. 78.1 -0.249 0.166 0.363 Not significant
#> 5 AND 41224. 84.4 1.31 0.002 0.0234 High-High
#> 6 ARE 41605. 81.6 0.966 0.012 0.0612 Not significant
#> 7 ARG 12774. 75.6 -0.0456 0.648 0.837 Not significant
#> 8 ARM 5378. 74.8 -0.0621 0.792 0.926 Not significant
#> 9 ATG 18305. 74.4 0.00203 0.906 0.964 Not significant
#> 10 AUS 61486. 68.3 -1.26 0.196 0.394 Not significant
#> # ℹ 189 more rows
# }