iTOP (version 1.0.2)

rv.link.significance: Wrapper function to determine significance in the PC algorithm

Description

This function is a wrapper function around rv.pval(), such that it can easily be used with pc() from the pcalg package. If you have trouble installing the pcalg package, have a look at our vignette 'A quick start to iTOP'.

Usage

rv.link.significance(a, b, set, suffStat)

Arguments

a

Either an index or a string to identify dataset a.

b

Either an index or a string to identify dataset b.

set

Datasets that need to be partialized for. Set to NULL if there are none (i.e. if you're computing a regular, non-partial RV). If set consists of one dataset, then provide an index or a string to identify set. If set consists of multiple datasets, then provide a vector of indices or a vector of strings.

suffStat

A named list with two items: cors, which is the result from rv.cor.matrix(); and cors_perm, which is the result from run.permutations().

Value

The p-value.

Examples

# NOT RUN {
set.seed(2)
n = 100
p = 100
x1 = matrix(rnorm(n*p), n, p)
x2 = x1 + matrix(rnorm(n*p), n, p)
x3 = x2 + matrix(rnorm(n*p), n, p)
data = list(x1=x1, x2=x2, x3=x3)
config_matrices = compute.config.matrices(data)
cors = rv.cor.matrix(config_matrices)
cors_perm = run.permutations(config_matrices, nperm=1000)

# }
# NOT RUN {
library(pcalg)
suffStat = list(cors=cors, cors_perm=cors_perm)
pc.fit = pc(suffStat=suffStat, indepTest=rv.link.significance, labels=names(data),
            alpha=0.05, conservative=TRUE, solve.confl=TRUE)
plot(pc.fit, main="")
# }