Calculate Bivariate Ripley's K function for IF data
This function calculates Ripley's K function of IF data for two markers.
bi_ripleys_k( mif, id, mnames, r_range = seq(0, 100, 50), num_permutations = 50, edge_correction = "translation", kestimation = TRUE, keep_perm_dis = FALSE, mlabels = NULL )
mif |
An MIF object |
id |
Character string of variable name for subject ID in TMA data. |
mnames |
A list of character strings containing two marker names |
r_range |
Numeric vector of potential r values to estimate K at. |
num_permutations |
Numeric value indicating the number of permutations used. Default is 50. |
edge_correction |
Character value indicating the type of edge correction to use. Options include "theoretical", "translation", "isotropic" or "border". Various edges corrections are most appropriate in different settings. Default is "none". |
kestimation |
Logical value determining the type estimation performed. TRUE estimates Ripley's reduced second moment function while FALSE estimates Besags's transformation of Ripley's K. |
keep_perm_dis |
Logical value determining whether or not to keep the full distribution of permuted K values |
mlabels |
A list of character strings containing two marker labels |
Returns a list of data frames
sample |
Subject ID in TMA data |
marker |
Ripley's K estimate using translation edge correction |
theoretical_estimate |
theoretical value of k |
observed_estimate |
observed estimate of k |
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