# local_jc_uni: Compute local univariate join count In sfdep: Spatial Dependence for Simple Features

 local_jc_uni R Documentation

## Compute 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_jc_uni(
fx,
chosen,
nb,
wt = st_weights(nb, style = "B"),
nsim = 499,
alternative = "two.sided",
iseed = NULL
)
```

### 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). `nb` a neighbors list object. `wt` default `st_weights(nb, style = "B")`. A binary weights list as created by `st_weights(nb, style = "B")`. `nsim` the number of conditional permutation simulations `alternative` default `"greater"`. One of `"less"` or `"greater"`. `iseed` default NULL, used to set the seed for possible parallel RNGs

### Details

The local join count statistic requires a binary weights list which can be generated with `st_weights(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. Calls `spdep::local_joincount_uni()`.

### Value

a `data.frame` with two columns `join_count` and `p_sim` and number of rows equal to the length of arguments `x`, `nb`, and `wt`.

### Examples

```res <- dplyr::transmute(
guerry,
top_crime = as.factor(crime_prop > 9000),
nb = st_contiguity(geometry),
wt = st_weights(nb, style = "B"),
jc = local_jc_uni(top_crime, "TRUE", nb, wt))
tidyr::unnest(res, jc)
```

sfdep documentation built on Jan. 11, 2023, 9:08 a.m.