R/plot.sox.R

In sox: Structured Learning in Time-Dependent Cox Models

#' Solution path plot for \code{sox()}
#'
#' @description
#' Plot the solution path generated by \code{\link{sox}()}.
#' 
#' @param x Fitted \code{\link{sox}} model.
#' @param type Graphical argument to be passed to \code{\link{matplot}()}, a character string (length 1 vector) or vector of 1-character strings indicating the type of plot for each column of y, see \link{plot.default} for all possible types. Default is "l" for lines.
#' @param log Graphical argument to be passed to \code{\link{matplot}()}, a character string which contains "x" if the x axis is to be logarithmic, "y" if the y axis is to be logarithmic, "" if neither, "xy" or "yx" if both axes are to be logarithmic. Default is "x".
#' @param ... Further arguments of \code{\link{matplot}()} and ultimately of \code{\link{plot.default}()} for some.
#' @return
#' Produces a coefficient profile plot of the coefficient paths for a fitted \code{\link{sox}} model.
#' 
#' @examples 
#' x <- as.matrix(sim[, c("A1","A2","C1","C2","B","A1B","A2B","C1B","C2B")])
#' lam.seq <- exp(seq(log(1e0), log(1e-3), length.out = 20))
#' 
#' overlapping.groups <- list(c(1, 2, 6, 7),
#'                            c(5, 6, 7, 8, 9),
#'                            c(6, 7),
#'                            c(3, 4, 8, 9),
#'                            c(8, 9))
#'                            
#' pars.overlapping <- overlap_structure(overlapping.groups)
#' 
#' fit.overlapping <- sox(
#'   x = x,
#'   ID = sim$Id,
#'   time = sim$Start,
#'   time2 = sim$Stop,
#'   event = sim$Event,
#'   penalty = "overlapping",
#'   lambda = lam.seq,
#'   group = pars.overlapping$groups,
#'   group_variable = pars.overlapping$groups_var,
#'   penalty_weights = pars.overlapping$group_weights,
#'   tol = 1e-4,
#'   maxit = 1e3,
#'   verbose = FALSE
#' )
#' 
#' plot(fit.overlapping)
#'               
#' cv.overlapping <- sox_cv(
#'   x = x,
#'   ID = sim$Id,
#'   time = sim$Start,
#'   time2 = sim$Stop,
#'   event = sim$Event,
#'   penalty = "overlapping",
#'   lambda = lam.seq,
#'   group = pars.overlapping$groups,
#'   group_variable = pars.overlapping$groups_var,
#'   penalty_weights = pars.overlapping$group_weights,
#'   nfolds = 5,
#'   tol = 1e-4,
#'   maxit = 1e3,
#'   verbose = FALSE
#' )
#' 
#' plot(cv.overlapping$sox.fit)
#'           
#' @seealso \code{\link{sox}}, \code{\link{sox_cv}}.
#' @method plot sox
#' @rdname plot.sox
#' @export

plot.sox <- function(x,
                     type = "l",
                     log = "x", ...) {
  lambdas <- x$lambdas
  
  estimates <- x$estimates
  
  matplot(x = lambdas,
          y = t(estimates),
          xlab = expression(paste(lambda)),
          ylab = "",
          type = type,
          log = log,
          ...)
  abline(h = 0, lty = 2)
}