View source: R/joincount-uni-impl.R

local_jc_uni | R Documentation |

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.

local_jc_uni( x, nb, wt = st_weights(nb, style = "B"), nsim = 499, alternative = "two.sided" )

`x` |
a binary variable either numeric or logical |

`nb` |
a neighbors list object. |

`wt` |
default |

`nsim` |
the number of conditional permutation simulations |

`alternative` |
default |

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.

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`

.

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

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