spdep (version 1.3-7)

local_joincount_uni: Calculate the local univariate join count

Description

The 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.

Usage

local_joincount_uni(
  fx,
  chosen,
  listw,
  alternative = "two.sided",
  nsim = 199,
  iseed = NULL,
  no_repeat_in_row=FALSE
)

Value

a data.frame with two columns BB and Pr() and number of rows equal to the length of x.

Arguments

fx

a binary variable either numeric or logical

chosen

a scalar character containing the level of fx that should be considered the observed value (1).

listw

a listw object containing binary weights created, for example, with nbwlistw(nb, style = "B")

alternative

default "greater". One of "less" or "greater".

nsim

the number of conditional permutation simulations

iseed

default NULL, used to set the seed; the output will only be reproducible if the count of CPU cores across which computation is distributed is the same

no_repeat_in_row

default FALSE, if TRUE, sample conditionally in each row without replacements to avoid duplicate values, https://github.com/r-spatial/spdep/issues/124

Author

Josiah Parry

Details

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.

References

Anselin, L., & Li, X. (2019). Operational Local Join Count Statistics for Cluster Detection. Journal of geographical systems, 21(2), 189–210. tools:::Rd_expr_doi("10.1007/s10109-019-00299-x")

Examples

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))