pval: Calculation of p-values for each score with respect to the...

In DiffNet: Identifying Significant Node Scores using Network Diffusion Algorithm

Calculation of p-values for each score with respect to the null.

Description

Calculation of p-values for each score with respect to the null.

Usage

pval(actual.scores, null.scores, method = "exponential")

Arguments

actual.scores	a vector including actual scores with the length of number of nodes (N_nodes).
null.scores	a matrix of null scores with the dimension of N_nodes x N_repeat
method	statistical test method: c("exponential", "gamma", "non_parametric")

Details

Calculate the p-value for each node based on the actual and null diffusion scores.

Value

vector of p-values

Examples

graph = graph_generation(n.nodes = 10, prob.connection = 0.5)
initial_score = c(rep(0,5),0.2, 0.3, 0, 0, 0.5)
names(initial_score) = igraph::V(graph)
Actual_score = actual_score(graph = graph, initial.score = initial_score, damping = 0.7)
Null_score = null_score(graph = graph, initial.score = initial_score, damping = 0.7, N.repeat = 10)
pvalue = pval(actual.scores = Actual_score, null.scores = Null_score, method = "exponential")

DiffNet documentation built on Nov. 22, 2023, 5:07 p.m.