Function for spatial association detector (SPADE) model.
Usage
spade(
formula,
data,
wt = NULL,
discvar = NULL,
discnum = 3:8,
discmethod = "quantile",
cores = 1,
seed = 123456789,
permutations = 0,
...
)
Arguments
- formula
A formula of spatial association detector (SPADE) model.
- data
A
data.frame
,tibble
orsf
object of observation data.- wt
(optional) The spatial weight matrix. When
data
is not ansf
object, must providewt
.- discvar
(optional) Name of continuous variable columns that need to be discretized. Noted that when
formula
hasdiscvar
,data
must have these columns. By default, all independent variables are used asdiscvar
.- discnum
(optional) Number of multilevel discretization. Default will use
3:8
.- discmethod
(optional) The discretization methods. Default all use
quantile
. Note that when using differentdiscmethod
fordiscvar
, please ensure that the lengths of both are consistent. Noted thatrobust
will userobust_disc()
;rpart
will userpart_disc()
; Others usesdsfun::discretize_vector()
.- cores
(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
- seed
(optional) Random number seed, default is
123456789
.- permutations
(optional) The number of permutations for the PSD computation. Default is
0
, which means no pseudo-p values are calculated.- ...
(optional) Other arguments passed to
sdsfun::discretize_vector()
,robust_disc()
orrpart_disc()
.
References
Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693
Author
Wenbo Lv lyu.geosocial@gmail.com
Examples
data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = spade(y ~ ., data = sim1)
g
#> *** Spatial Association Detector
#>
#> | variable | Q-statistic | P-value |
#> |:--------:|:-----------:|:-----------------:|
#> | xc | 0.5662369 | No Pseudo-P Value |
#> | xb | 0.4351567 | No Pseudo-P Value |
#> | xa | 0.3099817 | No Pseudo-P Value |