Global Join Counts — global_jc

Calculate global join count measure for a categorical variable.

Usage

global_jc_perm(
  fx,
  nb,
  wt,
  alternative = "greater",
  nsim = 499,
  allow_zero = FALSE,
  ...
)

global_jc_test(fx, nb, wt, alternative = "greater", allow_zero = NULL, ...)

tally_jc(fx, nb, wt, allow_zero = TRUE, ...)

Arguments

fx: a factor or character vector of the same length as nb.
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.
nsim: number of simulations to run.
allow_zero: If TRUE, assigns zero as lagged value to zone without neighbors.
...: additional arguments passed to methods

Value

an object of class jclist which is a list where each element is of class htest and mc.sim.

Details

global_jc_perm() implements the monte-carlo based join count using spdep::joincount.mc()
global_jc_test() implements the traditional BB join count statistic using spdep::joincount.test()
tally_jc() calculated join counts for a variable fx and returns a data.frame using spdep::joincount.multi()

Examples

geo <- sf::st_geometry(guerry)
nb <- st_contiguity(geo)
wt <- st_weights(nb, style = "B")
fx <- guerry$region
global_jc_perm(fx, nb, wt)
#> 
#> 	Monte-Carlo simulation of join-count statistic
#> 
#> data:  fx 
#> weights: listw 
#> number of simulations + 1: 500 
#> 
#> Join-count statistic for C = 35, rank of observed statistic = 500,
#> p-value = 0.002
#> alternative hypothesis: greater
#> sample estimates:
#>     mean of simulation variance of simulation 
#>               7.893788               5.781869 
#> 
#> 
#> 	Monte-Carlo simulation of join-count statistic
#> 
#> data:  fx 
#> weights: listw 
#> number of simulations + 1: 500 
#> 
#> Join-count statistic for E = 29, rank of observed statistic = 500,
#> p-value = 0.002
#> alternative hypothesis: greater
#> sample estimates:
#>     mean of simulation variance of simulation 
#>               7.875752               5.779712 
#> 
#> 
#> 	Monte-Carlo simulation of join-count statistic
#> 
#> data:  fx 
#> weights: listw 
#> number of simulations + 1: 500 
#> 
#> Join-count statistic for N = 29, rank of observed statistic = 500,
#> p-value = 0.002
#> alternative hypothesis: greater
#> sample estimates:
#>     mean of simulation variance of simulation 
#>               8.078156               6.305124 
#> 
#> 
#> 	Monte-Carlo simulation of join-count statistic
#> 
#> data:  fx 
#> weights: listw 
#> number of simulations + 1: 500 
#> 
#> Join-count statistic for S = 32, rank of observed statistic = 500,
#> p-value = 0.002
#> alternative hypothesis: greater
#> sample estimates:
#>     mean of simulation variance of simulation 
#>               7.831663               6.296907 
#> 
#> 
#> 	Monte-Carlo simulation of join-count statistic
#> 
#> data:  fx 
#> weights: listw 
#> number of simulations + 1: 500 
#> 
#> Join-count statistic for W = 31, rank of observed statistic = 500,
#> p-value = 0.002
#> alternative hypothesis: greater
#> sample estimates:
#>     mean of simulation variance of simulation 
#>               8.098196               6.570659 
#> 

global_jc_test(fx, nb, wt)
#> 
#> 	Join count test under nonfree sampling
#> 
#> data:  fx 
#> weights: listw 
#> 
#> Std. deviate for C = 10.886, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Same colour statistic           Expectation              Variance 
#>             35.000000              8.000000              6.151883 
#> 
#> 
#> 	Join count test under nonfree sampling
#> 
#> data:  fx 
#> weights: listw 
#> 
#> Std. deviate for E = 8.4667, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Same colour statistic           Expectation              Variance 
#>             29.000000              8.000000              6.151883 
#> 
#> 
#> 	Join count test under nonfree sampling
#> 
#> data:  fx 
#> weights: listw 
#> 
#> Std. deviate for N = 8.4667, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Same colour statistic           Expectation              Variance 
#>             29.000000              8.000000              6.151883 
#> 
#> 
#> 	Join count test under nonfree sampling
#> 
#> data:  fx 
#> weights: listw 
#> 
#> Std. deviate for S = 9.6763, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Same colour statistic           Expectation              Variance 
#>             32.000000              8.000000              6.151883 
#> 
#> 
#> 	Join count test under nonfree sampling
#> 
#> data:  fx 
#> weights: listw 
#> 
#> Std. deviate for W = 9.2731, p-value < 2.2e-16
#> alternative hypothesis: greater
#> sample estimates:
#> Same colour statistic           Expectation              Variance 
#>             31.000000              8.000000              6.151883 
#> 

tally_jc(fx, nb, wt)
#>      joincount expected  variance    z-value joins
#> C:C         35        8  6.151883  10.885785   C:C
#> E:E         29        8  6.151883   8.466721   E:E
#> N:N         29        8  6.151883   8.466721   N:N
#> S:S         32        8  6.151883   9.676253   S:S
#> W:W         31        8  6.151883   9.273076   W:W
#> E:C          8       17 12.887676  -2.507005   E:C
#> N:C          7       17 12.887676  -2.785561   N:C
#> N:E          8       17 12.887676  -2.507005   N:E
#> S:C          7       17 12.887676  -2.785561   S:C
#> S:E          5       17 12.887676  -3.342673   S:E
#> S:N          0       17 12.887676  -4.735454   S:N
#> W:C          9       17 12.887676  -2.228449   W:C
#> W:E          0       17 12.887676  -4.735454   W:E
#> W:N          3       17 12.887676  -3.899786   W:N
#> W:S          7       17 12.887676  -2.785561   W:S
#> Jtot        54      170 30.848971 -20.885152  Jtot