Compare spatial zone means

Usage

compare_zone(
  data,
  variable,
  zonesCol,
  alpha = 0.05,
  join = sf::st_nearest_feature,
  returnLSD = FALSE,
  grid_dim
)

Arguments

data: sf object with zones
variable: character or sf object to use for mean comparison
zonesCol: character colname from data were zone are specified
alpha: numeric Significance level to use for comparison
join: function to use for st_join if variable is sf object
returnLSD: logical when LSD calculates with spatial variance should be returned
grid_dim: numeric grid dimentins to estimate spatial variance

Value

list with differences and descriptive_stat

References

Paccioretti, P., Córdoba, M., & Balzarini, M. (2020). FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture, 175, 105556 https://doi.org/10.1016/j.compag.2020.105556.

Examples

library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
data(wheat, package = "paar")

##Convert to an sf object
wheat <- sf::st_as_sf(wheat,
                      coords = c("x", "y"),
                      crs = 32720)
clusters <- paar::kmspc(
  wheat,
  variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
  number_cluster = 3:4
)
data_clusters <- cbind(wheat, clusters$cluster)
compare_zone(data_clusters,
             "Elev",
             "Cluster_3")
#> $differences
#> $differences$Elev
#>   Cluster_3     Elev                       geometry stat
#> 3         3 160.2557 MULTIPOINT ((311982.8 58006...    a
#> 1         1 160.9515 MULTIPOINT ((311962.8 58006...    b
#> 2         2 161.2599 MULTIPOINT ((312192.8 58006...    c
#> 
#> 
#> $descriptive_stat
#> Key: <n>
#>        n         Elev
#>    <int>       <char>
#> 1:  5982 160.89 (0.3)
#>