R/X.pathway_discrete.imbalanced.R

In uni.survival.tree: A Survival Tree Based on Stabilized Score Tests for High-dimensional Covariates

#X.pathway_discrete.imbalanced
#' @title Generate a matrix of unbalance gene expressions (discrete version of X.pathway() against to Emura (2012)) in the presence of gene pathways
#' @description Generate a matrix of gene expressions in the presence of gene pathways, we first produce the matrix by X.pathway(Emura et al. 2012), then we change each value to 1 ~ 3 depend on the quantile and randomly replace a element to 4 in the last p-(q1+q2) column for each row.
#' @param n :the number of individuals (sample size)
#' @param p :the number of genes
#' @param q1 :the number of genes in the first pathway
#' @param q2 :the number of genes in the second pathway
#' @param rho1 :the correlation coefficient for the first pathway
#' @param rho2 :the correlation coefficient for the second pathway
#' @return X	n by p matrix of gene expressions
#' @examples
#' ## generate 6 gene expressions from 10 individuals
#' X.pathway_discrete.imbalanced(n=10,p=6,q1=2,q2=2,rho1=0.5,rho2=0.5)
#' @import compound.Cox
#' @references Emura T, Chen YH, Chen HY (2012). Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models. PLoS ONE 7(10): e47627. doi:10.1371/journal.pone.0047627
#' @export
X.pathway_discrete.imbalanced = function (n, p, q1, q2, rho1 = 0.5, rho2 = 0.5){
  X=X.pathway(n, p, q1, q2, rho1 , rho2 )
  if(p==q1+q2){
    warning("unbalanced covariate unsucessful")
  }
  r1 = 1/(1 + sqrt((1 - rho1)/rho1))
  r2 = 1/(1 + sqrt((1 - rho2)/rho2))
  SD0 = sqrt(3/4)
  SD1 = sqrt((3/4) * (2 * r1^2 - 2 * r1 + 1))
  SD2 = sqrt((3/4) * (2 * r2^2 - 2 * r2 + 1))
  for(i in 1:n){
    for(j in 1:p){
      if( j <= q1){
        xrange=seq(-1.5/SD1,1.5/SD1,0.05)
        quant=quantile(xrange,probs = c(0.333,0.666))
        quant33 = quant[1];quant66 = quant[2]
        if(X[i,j] < quant33){X[i,j]=1}
        else if(X[i,j] >= quant33 && X[i,j] < quant66){X[i,j]=2}
        else{X[i,j]=3}
      }
      else if( j >q1 && j<=q1+q2){
        xrange=seq(-1.5/SD2,1.5/SD2,0.05)
        quant=quantile(xrange,probs = c(0.333,0.666))
        quant33 = quant[1];quant66 = quant[2]
        if(X[i,j] < quant33){X[i,j]=1}
        else if(X[i,j] >= quant33 && X[i,j] < quant66){X[i,j]=2}
        else{X[i,j]=3}
      }
      else{
        xrange=seq(-1.5/SD0,1.5/SD0,0.05)
        quant=quantile(xrange,probs = c(0.333,0.666))
        quant33 = quant[1];quant66 = quant[2]
        if(X[i,j] < quant33){X[i,j]=1}
        else if(X[i,j] >= quant33 && X[i,j] < quant66){X[i,j]=2}
        else{X[i,j]=3}
      }
    }
  }
  xseq=sample(n,p-(q1+q2),replace = T)
  for(k in (q1+q2+1):p){
    X[xseq[k-(q1+q2)],k]=4
  }
  return(X)
}