R/bayesianFR.R

In gcostaneto/envirotype: GIS platforms to support envirorytping in breeding programs

bayesFR = function(.path = NULL,
                   df, W, y,
                   Kernel.G = NULL, 
                   Kernel.E = NULL, 
                   gid,env,
                   g.model  = "FIXED",
                   w.model  = "FIXED",
                   e.model  = "FIXED", 
                   ge.model = "FIXED",
                   nIter    = 15E3,
                   burnIn   = 5E3,
                   thin     = 10,
                   R2       = .6,
                   dir.names = NULL,
                   verbose  = FALSE){
  
  if(is.null(dir.names)){dir.names="output_dataframes"}
  homedir = getwd()
  if(is.null(.path)){.path = getwd()}
  dir = paste0(.path,"/",dir.names)
  cat(paste0("creating directory ", dir, "\n"));
  dir.create(dir)
  setwd(dir)
  
  .outut = vector(mode = "list",length = ncol(W))
  names(.outut) = colnames(W)
  namesC = colnames(W)
  
  for(COV in 1:ncol(W)){
  
   covDir = paste0("BGLR_",namesC[COV])
   dir.create(covDir)
   setwd(covDir)
   
    model = bGREG(df = df,
                  W = W[,COV],
                  y = y,
                  Kernel.G =Kernel.G,
                  Kernel.E = Kernel.E,gid = gid,env = env,
                  g.model = g.model,w.model = w.model,e.model = e.model,
                  ge.model = ge.model,nIter = nIter,burnIn = burnIn,
                  thin = thin,R2 = R2,verbose = verbose)
   setwd(dir)
    
    saveRDS(object = model$df,file = as.character(paste0("output_",namesC[COV]))) 
    
    .outut[[COV]] = model$mixed.solve
  }
  return(.outut)
}