Calculate the local Geary statistic for a given variable.

## Usage

local_g(x, nb, wt, alternative = "two.sided", ...)

local_g_perm(x, nb, wt, nsim = 499, alternative = "two.sided", ...)

## Arguments

x

A numeric vector.

nb

a neighbor list object for example as created by st_contiguity().

wt

a weights list as created by st_weights().

alternative

default "two.sided". Should be one of "greater", "less", or "two.sided" to specify the alternative hypothesis.

...

methods passed to spdep::localG() or spdep::localG_perm()

nsim

The number of simulations to run.

## Value

a data.frame with columns:

• gi: the observed statistic

• cluster: factor variable with two levels classification high or low

• e_gi: the permutation sample mean

• var_gi: the permutation sample variance

• std_dev: standard deviation of the Gi statistic

• p_value: the p-value using sample mean and standard deviation

• p_folded_sim: p-value based on the implementation of Pysal which always assumes a two-sided test taking the minimum possible p-value

• skewness: sample skewness

• kurtosis: sample kurtosis

## Examples

x <- guerry\$crime_pers
nb <- st_contiguity(guerry)
wt <- st_weights(nb)

res <- local_g_perm(x, nb, wt)

#>           gi cluster       e_gi       var_gi    std_dev    p_value p_sim
#> 1  0.8991367    High 0.01181669 5.294820e-06  0.8701837 0.38420001 0.424
#> 2  2.4455980    High 0.01191097 2.889082e-06  2.4734589 0.01338122 0.008
#> 3  2.1976551    High 0.01190228 2.679746e-06  2.3117279 0.02079268 0.036
#> 4 -1.5838690     Low 0.01188818 4.847045e-06 -1.5148402 0.12981289 0.116
#> 5 -1.1902425     Low 0.01187490 6.603139e-06 -1.1359518 0.25597672 0.252
#> 6 -1.6527255     Low 0.01185289 2.372027e-06 -1.6352541 0.10199579 0.104
#>   p_folded_sim    skewness    kurtosis
#> 1        0.212  0.06130549 -0.23895408
#> 2        0.004 -0.13947731 -0.09884521
#> 3        0.018  0.21507002  0.01754043
#> 4        0.058  0.09450422 -0.30518697
#> 5        0.126  0.22713868  0.04683920
#> 6        0.052  0.16068469  0.07086942