simu

library("cqcs")
library("doParallel")

library("cqcs")
library("doParallel")
num_cores = detectCores() - 4 
registerDoParallel(num_cores)

mtds  =  list(
  #censored quantile correlation screening
  cqcs  =  function(y, d, x, tau, len  =  NULL) {
    return(cqcs(y, d, x, tau, len))
  },
  #all observations quantile correlation screening
  aocqs  =  function(y, d, x, tau, len  =  NULL) {
    return(qcs(y, x, tau, len))
  },
  #completed observation based correlation screening
  cocqs  =  function(y, d, x, tau, len  =  NULL) {
    return(coqcs(y, d, x, tau, len))
  }
)

lngth = function(order) {
  max(which(order == 1),  which(order == 3),  which(order == 5))
}

tpn = function(y) {
  1 %in% y +  3 %in% y +  5 %in% y
}

# define  parameter sets
ms = 1:200
ns = c(100, 200)
ps  =  c(100, 200, 500)
heters = c(0, 1)
taus = c(0.25, 0.5, 0.75)
distris = c("normal", "cauchy")

m = 1
n = 100
p  =  2000
heter = 0
tau = 0.5
distri = 1
method = 1

foreach = foreach::foreach
datmat_foreach = foreach(m  =  ms,
                         .combine  =  "rbind",
                         .packages  =  c("MASS", "cqcs")) %:%
  foreach(n  =  ns, .combine  =  "rbind") %:%
  foreach(p  =  ps, .combine  =  "rbind") %:%
  foreach(heter  =  heters, .combine  =  "rbind") %:%
  foreach(tau  =  taus, .combine  =  "rbind") %:%
  foreach(distri  =  seq_along(distris), .combine  =  "rbind") %:%
  foreach(method  =  seq_along(mtds), .combine  =  "rbind") %dopar% {
    ## generate datasets
    set.seed(m)
    dat = cqcs::datagen(
      n = n,
      p = p,
      tau = tau,
      distri = distris[distri],
      heter = heter
    )
    y = dat$y
    d = dat$d
    x = dat$x
    
    ##orders
    orders  =  mtds[[method]](y, d, x, tau, p)
    len  =  lngth(orders)
    ## true positive
    orderlogn = orders[1:floor(n / log(n))]
    tp = tpn(orderlogn)
    ## return
    rst = list(
      m = m,
      n = n,
      p = p,
      heter = heter,
      tau = tau,
      distri = distris[distri],
      method = names(mtds)[method],
      rate = dat$rate,
      length = len,
      tp = tp
    )
  }

stopImplicitCluster()
write.csv(datmat_foreach, file  =  "result.csv", row.names  =  FALSE)