newFocus {newFocus}

The new focus level procedure

Description

The new focus level procedure for calculating true discoveries for focus level nodes

Usage

newFocus(response, fsets, null, data, maxit = 0, alpha = 0.05, adj = 0)

Arguments

response

The response variable

fsets

A list of focus level sets

null

The null hypothesis

data

The data frame with response and all covariates included

maxit

The maximal number of repetitions prespecified by user

alpha

The significance level

adj

The number of focus sets that are fully rejected by partial closed testing, which is used to adjust the number of focus sets, The dafault value is 0.

Value

The function will return a focus subject with the lower bound for each focus level node.

Author(s)

Ningning Xu

References

Goeman, J. J., & Mansmann, U. (2008). Multiple testing on the directed acyclic graph of gene ontology. Bioinformatics, 24(4), 537-544.

Examples

## example data set
n= 100
m = 5
X = matrix(0, n, m,byrow = TRUE )
for ( i in 1:n){
  set.seed(1234+i)
  X[i,] =  as.vector(arima.sim(model = list(order = c(1, 0, 0), ar = 0.2), n = m) )
}
y = rbinom(n,1,0.6)
X[which(y==1),1:3] = X[which(y==1),1:3] + 0.8
xs = paste("x",seq(1,m,1),sep="") 
colnames(X) = xs

mydata = as.data.frame(cbind(X,y))

## focus level sets
fl = list(c("x1", "x2"), c("x3", "x4"), "x5")
names(fl) = c("12", "34", "5")

## get td for focus level sets
focus_subject = newFocus(response = y, fsets = fl, data = mydata)

## get td for any set of interest given the focus subject
setofinterest = c("x1", "x2","x3", "x4")
pick(focus_subject, setofinterest)