Estimate Ripley's reduced second moment function for each feature in a spatial grid. — compute_k

This method calculates Ripley's reduced second moment function, K(r), for each feature in a spatial grid.

# S4 method for STGrid
compute_k_ripley(
  object,
  rmax = 80,
  nlarge = 2e+06,
  var.approx = FALSE,
  ratio = FALSE,
  sampling_rate = 0.25,
  seed = 123,
  verbose = TRUE
)

Arguments

object: An object of class "STGrid".
rmax: Maximum desired value of the argument r.
nlarge: Efficiency threshold. If the number of points exceeds nlarge, then only the border correction will be computed (by default), using a fast algorithm.
var.approx: Logical. If TRUE, the approximate variance of K(r) under CSR will also be computed.
ratio: Logical. If TRUE, the numerator and denominator of each edge-corrected estimate will also be saved, for use in analyzing replicated point patterns.
sampling_rate: By default a subset of molecules is sampled. Set sampling_rate to 1 to keep all molecules.
seed: The seed for sampling_rate.
verbose: Whether to print porgress bar.

Value

An updated object of class "STGrid" with the Ripley's K function estimates stored in the slot 'ripley_k_function'.

Examples

example_dataset()
#> |-- INFO :  Dataset 11284233/files/Xenium_Mouse_Brain_Coronal_7g was already loaded. 
Xenium_Mouse_Brain_Coronal_7g <- compute_k_ripley(Xenium_Mouse_Brain_Coronal_7g, verbose=FALSE)
#> |-- INFO :  >>> Estimating Ripley's reduced second moment function for all features.