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+#' @title Train an already fitted model on new data
+#'
+#' @title This function can be used to train an existing model on new data or 
+#' fit an existing model with slightly different parameters. It is useful for 
+#' retraining without having to copy all the parameters over. One common 
+#' application for this is to refit the best model found by a grid search, as 
+#' illustrated in the examples.
+#'
+#' @param fit Fitted \code{gensvm} object
+#' @param X Data matrix of the new data
+#' @param y Label vector of the new data
+#' @param verbose Turn on verbose output and fit progress. If NULL (the 
+#' default) the value from the fitted model is chosen.
+#'
+#' @return a new fitted \code{gensvm} model
+#'
+#' @export
+#'
+#' @author
+#' Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
+#' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
+#'
+#' @references
+#' Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized 
+#' Multiclass Support Vector Machine}, Journal of Machine Learning Research, 
+#' 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
+#'
+#' @examples
+#' x <- iris[, -5]
+#' y <- iris[, 5]
+#'
+#' # fit a standard model and refit with slightly different parameters
+#' fit <- gensvm(x, y)
+#' fit2 <- gensvm.refit(x, y, epsilon=1e-8)
+#'
+#' # refit a model returned by a grid search
+#' grid <- gensvm.grid(x, y)
+#' fit <- gensvm.refit(fit, x, y, epsilon=1e-8)
+#'
+#' # refit on different data
+#' idx <- runif(nrow(x)) > 0.5
+#' x1 <- x[idx, ]
+#' x2 <- x[!idx, ]
+#' y1 <- y[idx]
+#' y2 <- y[!idx]
+#'
+#' fit1 <- gensvm(x1, y1)
+#' fit2 <- gensvm.refit(fit1, x2, y2)
+#'
+gensvm.refit <- function(fit, X, y, p=NULL, lambda=NULL, kappa=NULL, 
+                         epsilon=NULL, weights=NULL, kernel=NULL, gamma=NULL, 
+                         coef=NULL, degree=NULL, kernel.eigen.cutoff=NULL, 
+                         max.iter=NULL, verbose=NULL, random.seed=NULL)
+{
+    p <- if(is.null(p)) fit$p else p
+    lambda <- if(is.null(lambda)) fit$lambda else lambda
+    kappa <- if(is.null(kappa)) fit$kappa else kappa
+    epsilon <- if(is.null(epsilon)) fit$epsilon else epsilon
+    weights <- if(is.null(weights)) fit$weights else weights
+    kernel <- if(is.null(kernel)) fit$kernel else kernel
+    gamma <- if(is.null(gamma)) fit$gamma else gamma
+    coef <- if(is.null(coef)) fit$coef else coef
+    degree <- if(is.null(degree)) fit$degree else degree
+    kernel.eigen.cutoff <- (if(is.null(kernel.eigen.cutoff)) 
+                            fit$kernel.eigen.cutoff else kernel.eigen.cutoff)
+    max.iter <- if(is.null(max.iter)) fit$max.iter else max.iter
+    verbose <- if(is.null(verbose)) fit$verbose else verbose
+    random.seed <- if(is.null(random.seed)) fit$random.seed else random.seed
+
+    # Setting the error handler here is necessary in case the user interrupts 
+    # this call to gensvm. If we don't set the error handler, R will 
+    # unnecessarily drop to a browser() session. We reset the error handler 
+    # after the call to gensvm().
+    options(error=function() {})
+    newfit <- gensvm(X, y, p=p, lambda=lambda, kappa=kappa, epsilon=epsilon, 
+                     weights=weights, kernel=kernel, gamma=gamma, coef=coef,
+                     degree=degree, kernel.eigen.cutoff=kernel.eigen.cutoff,
+                     verbose=verbose, max.iter=max.iter, seed.V=coef(fit))
+    options(error=NULL)
+
+    return(newfit)
+}