R/XGpred_predict.R

In csmpv: Biomarker Confirmation, Selection, Modelling, Prediction, and Validation

#' Predicting Risk Group Classification for a New Data Set
#' 
#' @description The XGpred_predict function is designed to predict risk group classifications for a new data set. 
#' This prediction is based on the assumption that an XGpred object is available from a training data set and
#'  that the new data set is comparable to the training data set.
#' 
#' In scenarios where the new and training data sets are not directly comparable, a calibration cohort is required 
#' to ensure accurate risk group predictions.
#'
#' @param newdat A data matrix or a data frame where samples are in rows and features/traits are in columns. 
#' It should include all variables needed for the prediction model.
#' @param XGpredObj An XGpred object returned by the XGpred function. Although not all items are needed for 
#' prediction purposes, using the XGpred object as input is convenient.
#' @param scoreShift A calibration value to subtract from the current model score if needed.
#' @author Aixiang Jiang
#' @return A data frame containing XGpred_score, XGpred_prob, and XGpred_prob_class
#' @references 
#'  Aoki T, Jiang A, Xu A et al.,(2023) Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes 
#'  in Relapsed/Refractory Hodgkin Lymphoma. J Clin Oncol. 2023 Dec 19:JCO2301115. doi: 10.1200/JCO.23.01115. Epub ahead of print. 
#'  PMID: 38113419.

#' @examples
#' # Load in data sets:
#' data("datlist", package = "csmpv")
#' tdat = datlist$training
#' vdat = datlist$validation
#' 
#' # The function saves files locally. You can define your own temporary directory. 
#' # If not, tempdir() can be used to get the system's temporary directory.
#' temp_dir = tempdir()
#' # As an example, let's define Xvars, which will be used later:
#' Xvars = c("highIPI", "B.Symptoms", "MYC.IHC", "BCL2.IHC", "CD10.IHC", "BCL6.IHC")

#' # For given time-to-event outcome and Xvars, we can build up a binary risk classification:
#'  xgobj = XGpred(data = tdat, varsIn = Xvars, 
#'                time = "FFP..Years.",
#'                 event = "Code.FFP", outfile = paste0(temp_dir, "/XGpred"))
#'  tdat$XGpred_class = xgobj$XGpred_prob_class
#' # You might save the files to the directory you want.
#' 
#'# Now, we can predict the risk classification for a new data set:
#'  xgNew = XGpred_predict(newdat = vdat, XGpredObj = xgobj)
#'
#' #' # To delete the "temp_dir", use the following:
#' unlink(temp_dir)


#' @export

XGpred_predict = function(newdat = NULL, XGpredObj = NULL, scoreShift = 0){
 
  wts = XGpredObj$weights
  XGpred_score = data.matrix(newdat[,names(wts)]) %*% wts
  
  XGpred_score = XGpred_score - scoreShift
  
  ## the following line is changed accordingly
  XGpred_prob = getProb(XGpred_score, groupMeans = ( XGpredObj$modelPars)[,1], groupSds = ( XGpredObj$modelPars)[,2])
  
  nclass = XGpredObj$nclass
  if(nclass == 3){
    XGpred_prob_class = ifelse(XGpred_prob >= XGpredObj$probcut, "High", ifelse(XGpred_prob <= 1-XGpredObj$probcut, "Low", "Middle"))
  }else{
    XGpred_prob_class = ifelse(XGpred_prob >= XGpredObj$probcut, "High", "Low")
  }
  
  ## XGpred_score, XGpred_prob, XGpred_prob_class
  outs = data.frame(cbind(XGpred_score, XGpred_prob))
  colnames(outs) = c("XGpred_score", "XGpred_prob")
  outs$XGpred_prob_class = XGpred_prob_class[,1]
  
  return(outs)
}