R/utilities.R

In MLGL: Multi-Layer Group-Lasso

#'
#' Obtain a sparse matrix of the coefficients of the path
#'
#' @param x \code{\link{MLGL}} object
#' @param row "lambda" or "covariates". If row="covariates", each row of the output matrix represents a covariate else 
#' if row="lambda", it represents a value of lambda.
#'
#' @return a sparse matrix containing the estimated coefficients for different lambdas
#'
#' @details This function can be used with a \code{\link{MLGL}} object to obtain a matrix with all estimated coefficients 
#' for the p original variables.
#' In case of overlapping groups, coefficients from repeated variables are summed.
#'
#' @examples
#' # Simulate gaussian data with block-diagonal variance matrix containing 12 blocks of size 5
#' X <- simuBlockGaussian(50, 12, 5, 0.7)
#' # Generate a response variable
#' y <- X[, c(2, 7, 12)] %*% c(2, 2, -2) + rnorm(50, 0, 0.5)
#' # Apply MLGL method
#' res <- MLGL(X, y)
#' # Convert output in sparse matrix format
#' beta <- listToMatrix(res)
#' @seealso \link{MLGL}, \link{overlapgglasso}
#'
#' @export
listToMatrix <- function(x, row = c("covariates", "lambda")) {
  row <- match.arg(row)
  if (row == "covariates") {
    bet <- Matrix(0, ncol = length(x$lambda), nrow = x$dim[2])
    for (i in seq_along(x$lambda))
    {
      dup <- duplicated(x$var[[i]])
      bet[x$var[[i]][!dup], i] <- bet[x$var[[i]][!dup], i] + x$beta[[i]][!dup]
      bet[x$var[[i]][dup], i] <- bet[x$var[[i]][dup], i] + x$beta[[i]][dup]
    }

    return(bet)
  }
  else {
    bet <- Matrix(0, nrow = length(x$lambda), ncol = x$dim[2])
    for (i in seq_along(x$lambda))
    {
      dup <- duplicated(x$var[[i]])
      bet[i, x$var[[i]][!dup]] <- bet[i, x$var[[i]][!dup]] + x$beta[[i]][!dup]
      bet[i, x$var[[i]][dup]] <- bet[i, x$var[[i]][dup]] + x$beta[[i]][dup]
    }

    return(bet)
  }
}