| true discoveries | R Documentation | 
To count the number of true discoveries within a given pathway or feature set of interest.
  discoveries (y, X, xs, hyps, maxit = 0, alpha = 0.05)
| 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. | 
Returns a non-negative interger.
Ningning Xu
Maintainer: Ningning Xu <n.xu@lumc.nl; xu15263142750@gmail.com>
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
  #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
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