The pair correlation function g(r) is the derivative of Ripley's K, so it measures clustering at a radius rather than cumulatively up to it. It is correspondingly slower to compute and noisier at small r.
xloc and yloc, if NULL, are taken as the midpoints of XMin/XMax and
YMin/YMax.
Usage
pair_correlation(
mif,
mnames,
r_range = NULL,
num_permutations = 100,
edge_correction = "translation",
keep_permutation_distribution = FALSE,
workers = 1,
overwrite = FALSE,
xloc = NULL,
yloc = NULL,
...
)Arguments
- mif
object of class
mif- mnames
character vector of marker names
- r_range
numeric vector including 0. If
NULL,spatstatchooses the range.- num_permutations
integer number of permutations used to estimate CSR
- edge_correction
edge correction passed to
spatstat.explore::pcf()- keep_permutation_distribution
boolean; keep each permutation's result or average them to one row per marker and radius
- workers
integer number of CPU cores used to process samples in parallel
- overwrite
boolean; replace an existing
univariate_pair_correlationslot rather than appending it as a newRun- xloc, yloc
the x and y columns giving cell centres. If left
NULL,XMin,XMax,YMinandYMaxmust be present.- ...
other parameters passed to
spatstat.explore::pcf(), plus support for deprecated argument names (see Details).
Output columns
As of 2.0.0 this function returns the same columns as ripleys_k(), with
Observed g in place of Observed K. Theoretical g and Permuted g are now
Theoretical CSR and Permuted CSR, Degree of Correlation * is now
Degree of Clustering *, and Permuted_larger_than_Observed is now
Permutations Larger than Observed. Exact CSR is present but always NA:
the closed-form CSR shortcut available to ripleys_k() has not been
implemented for the pair correlation function yet.
