local_joincount_uni.RdThe univariate local join count statistic is used to identify clusters of rarely occurring binary variables. The binary variable of interest should occur less than half of the time.
local_joincount_uni(
fx,
chosen,
listw,
alternative = "two.sided",
nsim = 199,
iseed = NULL
)a binary variable either numeric or logical
a scalar character containing the level of fx that should be considered the observed value (1).
a listw object containing binary weights created, for example, with nbwlistw(nb, style = "B")
default "greater". One of "less" or "greater".
the number of conditional permutation simulations
default NULL, used to set the seed for possible parallel RNGs
a data.frame with two columns BB and Pr() and number of rows equal to the length of x.
The local join count statistic requires a binary weights list which can be generated with nb2listw(nb, style = "B"). Additionally, ensure that the binary variable of interest is rarely occurring in no more than half of observations.
P-values are estimated using a conditional permutation approach. This creates a reference distribution from which the observed statistic is compared. For more see Geoda Glossary.
Anselin, L., & Li, X. (2019). Operational Local Join Count Statistics for Cluster Detection. Journal of geographical systems, 21(2), 189–210. doi:10.1007/s10109-019-00299-x
data(oldcol)
fx <- as.factor(ifelse(COL.OLD$CRIME < 35, "low-crime", "high-crime"))
listw <- nb2listw(COL.nb, style = "B")
set.seed(1)
(res <- local_joincount_uni(fx, chosen = "high-crime", listw))
#> BB Pr(z != E(BBi))
#> 1 0 NA
#> 2 0 NA
#> 3 4 0.50
#> 4 0 NA
#> 5 0 NA
#> 6 0 NA
#> 7 4 0.89
#> 8 0 NA
#> 9 5 0.11
#> 10 0 NA
#> 11 0 NA
#> 12 0 NA
#> 13 0 NA
#> 14 0 NA
#> 15 0 NA
#> 16 0 NA
#> 17 0 NA
#> 18 0 NA
#> 19 0 NA
#> 20 3 0.55
#> 21 2 0.84
#> 22 3 0.30
#> 23 4 0.61
#> 24 5 0.12
#> 25 0 NA
#> 26 0 NA
#> 27 0 NA
#> 28 0 NA
#> 29 3 0.98
#> 30 5 0.09
#> 31 8 0.01
#> 32 7 0.02
#> 33 6 0.02
#> 34 5 0.04
#> 35 7 0.03
#> 36 8 0.02
#> 37 6 0.05
#> 38 5 0.04
#> 39 3 0.44
#> 40 5 0.14
#> 41 3 0.13
#> 42 5 0.05
#> 43 2 0.63
#> 44 0 NA
#> 45 0 NA
#> 46 0 NA
#> 47 0 NA
#> 48 0 NA
#> 49 0 NA