diffuse_mc {diffuStats} | R Documentation |
Function diffuse_mc
has an implemented parallelisation of the
Monte Carlo trials for diffusion in a network.
The input scores are assumed to be sparse and are
internally sparsified, so very dense scores
migth take time with current implementation.
diffuse_mc( graph, scores, n.perm = 10000, sample.prob = NULL, seed = 1, oneminusHeatRank = TRUE, K = NULL, ... )
graph |
igraph object |
scores |
Recursive list, can have either binary or quantitative scores |
n.perm |
Numeric, number of permutations |
sample.prob |
Numeric, probabilities (needn't be scaled) to permute the
input. This is passed to |
seed |
Numeric, seed for random number generator |
oneminusHeatRank |
Logical, should |
K |
Kernel matrix (if precomputed). If |
... |
currently ignored arguments |
A list containing matrices of heatrank scores
# Using a list as input (needed) data(graph_toy) list_input <- list(myInput1 = graph_toy$input_mat) diff_mc <- diffuse_mc( graph = graph_toy, scores = list_input)