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( fx, chosen, nb, wt = st_weights(nb, style = "B"), nsim = 499, alternative = "two.sided", iseed = NULL )
fx |
a binary variable either numeric or logical |
chosen |
a scalar character containing the level of |
nb |
a neighbors list object. |
wt |
default |
nsim |
the number of conditional permutation simulations |
alternative |
default |
iseed |
default NULL, used to set the seed for possible parallel RNGs |
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()
.
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 = 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.