Skip to contents

Compare means between spatial zones

Source: R/compare_zone.R
compare_zone.Rd

Compares variable means across spatial zones using a spatially-adjusted least significant difference (LSD) approach based on kriging variance.

The function accounts for spatial variability by estimating semivariograms and deriving a spatial variance component, which is then used to assess differences between zone means.

Usage

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

Arguments

data

an sf object containing the spatial zones

variable

either:

  • a character vector with column names in data, or

  • an sf object with external variables to be compared. In this case, values are spatially joined to data.

zonesCol

character. Column name in data defining zones

alpha

numeric. Significance level for mean comparison

join

function used in sf::st_join when variable is an external sf object (default: sf::st_nearest_feature)

returnLSD

logical. If TRUE, returns the LSD value used for comparisons

grid_dim

numeric. Grid resolution used to estimate spatial variance when interpolating external variables. If missing, it is automatically determined.

Value

A list with:

differences

list of data frames with mean comparisons per variable

descriptive_stat

data frame with descriptive statistics and spatial variance

Details

When variable is an external sf object, values are interpolated using ordinary kriging before comparison. Otherwise, cross-validation of the variogram model is used to estimate spatial variance.

Pairwise comparisons between zones are evaluated using a spatially-adjusted LSD criterion:

$$LSD = z_{1-\alpha/2} \times \sigma_{spatial}$$

where \(\sigma_{spatial}\) is derived from kriging variance.

Results are presented using compact letter displays to indicate groups of zones that are not significantly different.

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. doi:10.1016/j.compag.2020.105556

Examples

library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; 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)
#>