TEAM {TEAM}

Testing on an Aggregation Tree Method

Description

This function performs multiple testing embedded in a hierarchical structure in order to identify local differences between two independent distributions (e.g. case and control).

Usage

TEAM(x1, x2, theta0 = length(x2)/length(c(x1, x2)), K = 14,
  alpha = 0.05, L = 3)

Arguments

x1

Numeric vector of N1 control observations

x2

Numeric vector of N2 case observations

theta0

Nominal boundary level for binomial parameter - default is N2/(N1+N2)

K

log2 number of bins

alpha

Nominal false discovery rate (FDR) level

L

Number of layers in the aggregation tree

Value

List containing the discoveries (S.list) in each layer and the estimated layer-specific thresholds (c.hats)

References

Pura J. Chan C. Xie J. Multiple Testing Embedded in an Aggregation Tree to Identify where Two Distributions Differ. https://arxiv.org/abs/1906.07757

Examples

set.seed(1)
# Simulate local shift difference for each population from mixture of normals
N1 <- N2 <- 1e6
require(ks) #loads rnorm.mixt function
#Controls
x1 <- rnorm.mixt(N1,mus=c(0.2,0.89),sigmas=c(0.04,0.01),props=c(0.97,0.03))
#Cases
x2 <- rnorm.mixt(N2,mus=c(0.2,0.88),sigmas=c(0.04,0.01),props=c(0.97,0.03))
res <- TEAM(x1,x2,K=14,alpha=0.05,L=3)
#Discoveries in each layer - Each element is an growing set of
#indices captured at each layer
res$S.list
#Map back final discoveries in layer 3 to corresponding regions
levels(res$dat$quant)[res$S.list[[3]]]