true discoveries {ctgt}

True discoveries

Description

To count the number of true discoveries within a given pathway or feature set of interest.

Usage

  discoveries (y, X, xs, hyps, maxit = 0, alpha = 0.05)

Arguments

y

The response vector (numeric vector).

X

The full design matrix, whose columns are named by the covariates.

xs

The name vector of all covariates (character vector).

hyps

The name vector of the covariates in the pathway of interest (character vector).

maxit

An optional integer to denote the maximal interations for branch and bound method. The default value 0 means the single-step shortcut without branch and bound method. Note that larger value is more time-consuming.

alpha

The type I error rate allowed. The default is 0.05.

Value

Returns a non-negative interger.

Author(s)

Ningning Xu

Maintainer: Ningning Xu <[e-mail]; [e-mail]>

References

Ningning Xu, Aldo solari, Jelle Goeman, Clsoed testing with global test, with applications on metabolomics data, arXiv:2001.01541, https://arxiv.org/abs/2001.01541

Examples

  #Generate the design matrix and response vector for logistic regression models
  n= 100
  m = 5
  X = matrix(data = 0, nrow = n, ncol = 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

  # For standarized data
  X = scale(x = X,center = FALSE,scale = TRUE)/sqrt(n-1)
  interest = xs

  discoveries(y=y, X = X, xs = xs, hyps = interest)
  #2
  discoveries(y=y, X = X, xs = xs, hyps = interest, maxit=10)
  #2