hypothesesTree: Class '"hypothesesTree"'

In structSSI: Multiple Testing for Hypotheses with Hierarchical or Group Structure

Description

Class for performing hierarchical multiple testing corrections.

Slots

tree:

Object of class "matrix". The edgelist for the hypotheses tree.

p.vals:

Object of class "data.frame". Each row correpsonds to an individual hypothesis. The first column stores the p-values before GBH adjustment, while the second gives the hFDR adjusted p-values. The hypotheses are sorted in order of significance according to these GBH adjusted p-values. The group column gives the group membership of each hypothesis, and adj.significnace codes the significance of each hypothesis, according to the GBH adjusted p-values.

alpha:

Object of class "numeric". The level at which the FDR is controlled among children of each parent node.

Methods

initialize

signature(.Object = "hypothesesTree"): ...

Check that the hypotheses tree is correctly initialized. It ensures that the number of unadjusted p-values, hypotheses names, and nodes in the hypotheses tree all agree. It also checks that the hypotheses tree is in fact a tree.

plot

signature(x = "hypothesesTree", y = "ANY"): ...

Plots the tree of hypotheses and their p-values either before or after adjustment. If a particular node hypothesis was not tested, it is colored grey. If it was tested and rejected, it is green; if it was tested and not rejected, it is shaded blue. The deeper the shade, the lower (more significant) the p-value was.

show

signature(object = "hypothesesTree"): ...

This prints the unadjusted and adjusted p-values of the hypotheses tree associated with the HFDR procedure.

summary

signature(object = "hypothesesTree"): This prints the most significant adjusted p-values, along with estimates of the FDR across the tree and at tips.

Author(s)

Kris Sankaran

See Also

hFDR.adjust EstimatedHFDRControl

Examples

library('igraph')
library('ape')

alternative.indices <- sample(1:49, 30)
unadj.p.values <- vector("numeric", length = 49)
unadj.p.values[alternative.indices] <- runif(30, 0, 0.01)
unadj.p.values[-alternative.indices] <- runif(19, 0, 1)
unadj.p.values[c(1:5)] <- runif(5, 0, 0.01)
names(unadj.p.values) <- paste("Hyp ", c(1:49))

tree <- as.igraph(rtree(25))
V(tree)$name <- names(unadj.p.values)
tree.el <- get.edgelist(tree)

hyp.tree <- hFDR.adjust(unadj.p.values, tree.el, 0.05)
plot(hyp.tree)

structSSI documentation built on May 2, 2019, 11:26 a.m.