R/LASSO2_reg.R

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

#' LASSO2 Variable Selection and Regular Regression Modeling
#' 
#' @description
#' This function performs variable selection with LASSO2 but ignores the shrunk LASSO coefficients and builds a regular regression model.
#' 
#' @details
#' The first part of LASSO2_reg involves variable selection with LASSO2, typically based on the mean lambda.1se from 10 iterations of 
#' n-fold cross-validation-based LASSO regression. In each iteration, a lambda.1se refers to the largest value of lambda such that the error 
#' is within 1 standard error of the minimum. However, if only one or no variable is selected, the cross-validation results are ignored, 
#' and a minimum of two remaining variables is ensured through full-data lambda simulations.
#' The second part of LASSO2_reg involves ignoring the shrunk LASSO coefficients and building a regular regression model.
#' It is suitable for three types of outcomes: continuous, binary, and time-to-event.
#' @param data A data matrix or a data frame where samples are in rows, and features/traits are in columns.
#' @param standardization A logical variable to indicate if standardization is needed before variable selection. The default is FALSE.
#' @param columnWise A logical variable to indicate if column-wise or row-wise normalization is needed. The default is TRUE, which performs column-wise normalization. 
#'        This is only meaningful when "standardization" is TRUE.
#' @param biomks A vector of potential biomarkers for variable selection. They should be a subset of column names in "data". 
#' @param outcomeType The outcome variable type. There are three choices: "binary" (default), "continuous", and "time-to-event".
#' @param Y The outcome variable name when the outcome type is either "binary" or "continuous".
#' @param time The time variable name when the outcome type is "time-to-event".
#' @param event The event variable name when the outcome type is "time-to-event".
#' @param nfolds The number of folds for cross-validation. The default value is 10.
#' @param outfile A string for the output file including the path, if necessary, but without the file type extension.
#' @importFrom glmnet cv.glmnet
#' @importFrom Matrix nnzero
#' @author Aixiang Jiang
#' 
#' @return 
#' A list is returned with the same output as from confirmVars.
#' \item{fit}{A model with selected variables for the given outcome variable.}
#' \item{allplot}{A list with all plots.}
#' 
#' @references 
#' Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, doi:10.18637/jss.v033.i01.
#' 
#' Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, doi:10.18637/jss.v039.i05.
#' 
#' Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
#' 
#' Therneau, T., Grambsch, P., Modeling Survival Data: Extending the Cox Model. Springer-Verlag, 2000.
#' 
#' Kassambara A, Kosinski M, Biecek P (2021). survminer: Drawing Survival Curves using 'ggplot2'_. R package version 0.4.9,
#'
#' @examples
#' # Load in data sets:
#' data("datlist", package = "csmpv")
#' tdat = datlist$training
#' 
#' # 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")
#'
#' # The function can work with three different outcome types. 
#' # Here, we use time-to-event as an example:

#' # tlr = LASSO2_reg(data = tdat, biomks = Xvars,
#' #                  outcomeType = "time-to-event",
#' #                  time = "FFP..Years.",event = "Code.FFP",
#' #                  outfile = paste0(temp_dir, "/survivalLASSO2_reg"))

#' # You might save the files to the directory you want.
#' # To delete the "temp_dir", use the following:
#' unlink(temp_dir)

#' @export

LASSO2_reg = function(data = NULL, standardization = FALSE, columnWise = TRUE, biomks = NULL, outcomeType = c("binary","continuous","time-to-event"), 
                      Y = NULL, time = NULL, event = NULL, nfolds = 10, outfile = "nameWithPath"){
  
  if(is.null(data)){
    stop("Please input a data set")
  }
  if(standardization){
    data = standardize(data, byrow = !columnWise)
  }
  
  alls = NA
  
  outcomeType = outcomeType[1]
  
  lasres = LASSO2(data = data, biomks = biomks, outcomeType = outcomeType, 
                 Y = Y, time = time, event = event, nfolds = nfolds, outfile = outfile)
  svars = names(lasres$coefs)
  
  if(length(svars) > 0){
    fit = confirmVars(data = data, standardization = FALSE, columnWise = TRUE, biomks = svars,  
                    outcomeType = outcomeType, Y = Y, time = time, event = event, outfile = paste0(outfile, "_LASSO_reg"))
  }
  return(fit)
}