Information-Theoretic Measures for Spatial Association

Usage

itm(
  formula,
  data,
  method = c("vm", "icm"),
  beta = 1,
  unit = c("e", "2", "10"),
  seed = 42,
  permutation_number = 999
)

Arguments

formula: A formula.
data: A data.frame, tibble or sf object of observation data.
method: (optional) whether vm(default) or icm.
beta: (optional) The \(\beta\) value used fo vm measure, default is 1.
unit: (optional) Logarithm base, default is e.
seed: (optional) Random number seed, default is 42.
permutation_number: (optional) Number of Random Permutations, default is 999.

Value

A tibble.

Examples

sim = readr::read_csv(system.file('extdata/sim.csv',package = 'itmsa'))
#> Rows: 80 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (4): z1, z2, x, y
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# \donttest{
# Information-theoretical V-measure
itm(z1 ~ z2, data = sim, method = 'vm')
#> # A tibble: 1 × 3
#>   Variable    Iv    Pv
#>   <chr>    <dbl> <dbl>
#> 1 z2       0.361     0
# Information Consistency-Based Measures
itm(z1 ~ z2, data = sim, method = 'icm')
#> # A tibble: 1 × 3
#>   Variable    Iv    Pv
#>   <chr>    <dbl> <dbl>
#> 1 z2       0.315     0
# }