Documentation improvements

24 files changed, 243 insertions, 59 deletions

diff --git a/R/gensvm.R b/R/gensvm.R
index 8e1242e..db023e2 100644
--- a/R/gensvm.R
+++ b/R/gensvm.R
@@ -13,6 +13,9 @@
 #' @param lambda regularization parameter for the loss function (lambda > 0)
 #' @param kappa parameter for the hinge function in the loss function (kappa >
 #' -1.0)
+#' @param epsilon Stopping parameter for the optimization algorithm. The 
+#' optimization will stop if the relative change in the loss function is below 
+#' this value.
 #' @param weights type or vector of instance weights to use. Options are 'unit'
 #' for unit weights and 'group' for group size correction weights (eq. 4 in the
 #' paper). Alternatively, a vector of weights can be provided.
@@ -92,7 +95,7 @@
 #' fit <- gensvm(x, y)
 #'
 #' # fit and show progress
-#' fit <- gensvm(x, y, verbose=T)
+#' fit <- gensvm(x, y, verbose=TRUE)
 #'
 #' # fit with some changed parameters
 #' fit <- gensvm(x, y, lambda=1e-8)
diff --git a/R/gensvm.accuracy.R b/R/gensvm.accuracy.R
index c762dd7..387c2db 100644
--- a/R/gensvm.accuracy.R
+++ b/R/gensvm.accuracy.R
@@ -1,8 +1,13 @@
 #' @title Compute the accuracy score
 #'
+#' @description Compute the accuracy score between the true labels and the 
+#' predicted labels.
+#'
 #' @param y.true vector of true labels
 #' @param y.pred vector of predicted labels
 #'
+#' @return The accuracy as a value in the range [0.0, 1.0]
+#'
 #' @author
 #' Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
 #' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
diff --git a/R/gensvm.grid.R b/R/gensvm.grid.R
index 7be065b..755d650 100644
--- a/R/gensvm.grid.R
+++ b/R/gensvm.grid.R
@@ -362,11 +362,29 @@ gensvm.load.small.grid <- function()
 }
 
 
-#' Generate a vector of cross-validation indices
+#' @title Generate a vector of cross-validation indices
 #'
+#' @description
 #' This function generates a vector of length \code{n} with values from 0 to 
 #' \code{folds-1} to mark train and test splits.
 #'
+#' @param n the number of instances
+#' @param folds the number of cross validation folds
+#'
+#' @return an array of length \code{n} with values in the range [0, folds-1] 
+#' indicating the test fold of each instance.
+#'
+#' @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}.
+#'
+#' @seealso
+#' \code{\link{gensvm.grid}}
 gensvm.generate.cv.idx <- function(n, folds)
 {
     cv.idx <- matrix(0, n, 1)
@@ -572,13 +590,14 @@ gensvm.expand.param.grid <- function(pg, n.features)
 
 #' @title Compute the ranks for the numbers in a given vector
 #'
-#' @details
-#' This function computes the ranks for the values in an array. The highest 
-#' value gets the smallest rank. Ties are broken by assigning the smallest 
-#' value.
+#' @description This function computes the ranks for the values in an array. 
+#' The highest value gets the smallest rank. Ties are broken by assigning the 
+#' smallest value. The smallest rank is 1.
 #'
 #' @param x array of numeric values
 #'
+#' @return array with the ranks of the values in the input array.
+#'
 gensvm.rank.score <- function(x)
 {
     x <- as.array(x)
diff --git a/R/gensvm.refit.R b/R/gensvm.refit.R
index b7a1a15..f3b818b 100644
--- a/R/gensvm.refit.R
+++ b/R/gensvm.refit.R
@@ -1,16 +1,40 @@
 #' @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.
+#' @description 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 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.
+#' @param p if NULL use the value from \code{fit} in the new model, otherwise 
+#' override with this value.
+#' @param lambda if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param kappa if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param epsilon if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param weights if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param kernel if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param gamma if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param coef if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param degree if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param kernel.eigen.cutoff if NULL use the value from \code{fit} in the new 
+#' model, otherwise override with this value.
+#' @param max.iter if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param verbose if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
+#' @param random.seed if NULL use the value from \code{fit} in the new model, 
+#' otherwise override with this value.
 #'
 #' @return a new fitted \code{gensvm} model
 #'
@@ -50,7 +74,7 @@
 #' fit1 <- gensvm(x1, y1)
 #' fit2 <- gensvm.refit(fit1, x2, y2)
 #'
-gensvm.refit <- function(fit, X, y, p=NULL, lambda=NULL, kappa=NULL, 
+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)
@@ -76,7 +100,7 @@ gensvm.refit <- function(fit, X, y, p=NULL, lambda=NULL, kappa=NULL,
     # after the call to gensvm().
     errfunc <- getOption('error')
     options(error=function() {})
-    newfit <- gensvm(X, y, p=p, lambda=lambda, kappa=kappa, epsilon=epsilon, 
+    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))
diff --git a/R/gensvm.train.test.split.R b/R/gensvm.train.test.split.R
index eca4b06..cd40ecc 100644
--- a/R/gensvm.train.test.split.R
+++ b/R/gensvm.train.test.split.R
@@ -18,6 +18,12 @@
 #' of 0.25 will be used.
 #' @param shuffle shuffle the rows or not
 #' @param random.state seed for the random number generator (int)
+#' @param return.idx whether or not to return the indices in the output
+#'
+#' @return a list with \code{x.train} and \code{x.test} splits of the \code{x} 
+#' array provided. If \code{y} is provided, also \code{y.train} and 
+#' \code{y.test}. If \code{return.idx} is TRUE, also \code{idx.train} and 
+#' \code{idx.test}.
 #'
 #' @author
 #' Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
diff --git a/R/plot.gensvm.R b/R/plot.gensvm.R
index 129d90a..3f6f5d2 100644
--- a/R/plot.gensvm.R
+++ b/R/plot.gensvm.R
@@ -1,8 +1,8 @@
 #' @title Plot the simplex space of the fitted GenSVM model
 #' 
 #' @description This function creates a plot of the simplex space for a fitted 
-#' GenSVM model and the given data set, as long as the dataset consists of only 
-#' 3 classes.  For more than 3 classes, the simplex space is too high 
+#' GenSVM model and the given data set. This function works for dataset with 
+#' two or three classes. For more than 3 classes, the simplex space is too high 
 #' dimensional to easily visualize.
 #'
 #' @param x A fitted \code{gensvm} object
@@ -60,8 +60,14 @@
 #' # plot only misclassified samples
 #' x.mis <- x[predict(fit) != y, ]
 #' y.mis.true <- y[predict(fit) != y]
-#' plot(fit, x.test=x.mis)
-#' plot(fit, y.mis.true, x.test=x.mis)
+#' plot(fit, newdata=x.mis)
+#' plot(fit, y.mis.true, newdata=x.mis)
+#'
+#' # plot a 2-d model
+#' xx <- x[y %in% c('versicolor', 'virginica'), ]
+#' yy <- y[y %in% c('versicolor', 'virginica')]
+#' fit <- gensvm(xx, yy, kernel='rbf', max.iter=5000)
+#' plot(fit)
 #'
 plot.gensvm <- function(x, labels, newdata=NULL, with.margins=TRUE, 
                         with.shading=TRUE, with.legend=TRUE, center.plot=TRUE, 
diff --git a/R/validate.R b/R/validate.R
index 5960a38..c708158 100644
--- a/R/validate.R
+++ b/R/validate.R
@@ -1,7 +1,14 @@
 #' @title [internal] Validate parameters
 #'
+#' @description Internal function used to validate the parameters passed to the 
+#' gensvm() function.
+#'
+#' @return TRUE if all values pass their respective conditions, FALSE 
+#' otherwise.
+#'
 #' @export
 #' @keywords internal
+#'
 gensvm.validate.params <- function(p=NULL, kappa=NULL, lambda=NULL, 
                                    epsilon=NULL, gamma=NULL, weights=NULL, 
                                    kernel=NULL, ...)
@@ -26,8 +33,15 @@ gensvm.validate.params <- function(p=NULL, kappa=NULL, lambda=NULL,
 
 #' @title [internal] Validate parameter grid
 #'
+#' @description Internal function to validate all parameters in a parameter 
+#' grid.
+#'
+#' @return TRUE if all values pass their respective conditions, FALSE 
+#' otherwise.
+#'
 #' @export
 #' @keywords internal
+#'
 gensvm.validate.param.grid <- function(df)
 {
     expected.colnames <- c("kernel", "coef", "degree", "gamma", "weights", 
diff --git a/man/coef.gensvm.Rd b/man/coef.gensvm.Rd
index 9f08ecc..aba3efe 100644
--- a/man/coef.gensvm.Rd
+++ b/man/coef.gensvm.Rd
@@ -4,10 +4,10 @@
 \alias{coef.gensvm}
 \title{Get the coefficients of the fitted GenSVM model}
 \usage{
-\method{coef}{gensvm}(fit, ...)
+\method{coef}{gensvm}(object, ...)
 }
 \arguments{
-\item{fit}{a \code{gensvm} object}
+\item{object}{a \code{gensvm} object}
 
 \item{\dots}{further arguments are ignored}
 }
diff --git a/man/fitted.gensvm.Rd b/man/fitted.gensvm.Rd
index b242eb8..c65123e 100644
--- a/man/fitted.gensvm.Rd
+++ b/man/fitted.gensvm.Rd
@@ -2,13 +2,12 @@
 % Please edit documentation in R/fitted.gensvm.R
 \name{fitted.gensvm}
 \alias{fitted.gensvm}
-\alias{fitted}
 \title{Show fitted labels for the GenSVM model}
 \usage{
-\method{fitted}{gensvm}(fit, ...)
+\method{fitted}{gensvm}(object, ...)
 }
 \arguments{
-\item{fit}{Fitted \code{gensvm} object}
+\item{object}{Fitted \code{gensvm} object}
 
 \item{\dots}{further arguments are passed to predict}
 }
diff --git a/man/fitted.gensvm.grid.Rd b/man/fitted.gensvm.grid.Rd
index 1e2f697..455df7e 100644
--- a/man/fitted.gensvm.grid.Rd
+++ b/man/fitted.gensvm.grid.Rd
@@ -2,13 +2,12 @@
 % Please edit documentation in R/fitted.gensvm.grid.R
 \name{fitted.gensvm.grid}
 \alias{fitted.gensvm.grid}
-\alias{fitted}
 \title{Fitted labels for the GenSVMGrid class}
 \usage{
-\method{fitted}{gensvm.grid}(grid, ...)
+\method{fitted}{gensvm.grid}(object, ...)
 }
 \arguments{
-\item{grid}{A \code{gensvm.grid} object}
+\item{object}{A \code{gensvm.grid} object}
 
 \item{\dots}{further arguments are passed to fitted}
 }
diff --git a/man/gensvm.Rd b/man/gensvm.Rd
index b6c9bf0..4f5d614 100644
--- a/man/gensvm.Rd
+++ b/man/gensvm.Rd
@@ -24,6 +24,10 @@ function (i.e. \code{model.matrix( ~ var - 1)}).}
 \item{kappa}{parameter for the hinge function in the loss function (kappa >
 -1.0)}
 
+\item{epsilon}{Stopping parameter for the optimization algorithm. The 
+optimization will stop if the relative change in the loss function is below 
+this value.}
+
 \item{weights}{type or vector of instance weights to use. Options are 'unit'
 for unit weights and 'group' for group size correction weights (eq. 4 in the
 paper). Alternatively, a vector of weights can be provided.}
@@ -99,7 +103,7 @@ y <- iris[, 5]
 fit <- gensvm(x, y)
 
 # fit and show progress
-fit <- gensvm(x, y, verbose=T)
+fit <- gensvm(x, y, verbose=TRUE)
 
 # fit with some changed parameters
 fit <- gensvm(x, y, lambda=1e-8)
diff --git a/man/gensvm.accuracy.Rd b/man/gensvm.accuracy.Rd
index 9463fa5..25b8fb7 100644
--- a/man/gensvm.accuracy.Rd
+++ b/man/gensvm.accuracy.Rd
@@ -11,6 +11,13 @@ gensvm.accuracy(y.true, y.pred)
 
 \item{y.pred}{vector of predicted labels}
 }
+\value{
+The accuracy as a value in the range [0.0, 1.0]
+}
+\description{
+Compute the accuracy score between the true labels and the 
+predicted labels.
+}
 \examples{
 x <- iris[, -5]
 y <- iris[, 5]
diff --git a/man/gensvm.generate.cv.idx.Rd b/man/gensvm.generate.cv.idx.Rd
index 38d8829..dbba19e 100644
--- a/man/gensvm.generate.cv.idx.Rd
+++ b/man/gensvm.generate.cv.idx.Rd
@@ -6,7 +6,28 @@
 \usage{
 gensvm.generate.cv.idx(n, folds)
 }
+\arguments{
+\item{n}{the number of instances}
+
+\item{folds}{the number of cross validation folds}
+}
+\value{
+an array of length \code{n} with values in the range [0, folds-1] 
+indicating the test fold of each instance.
+}
 \description{
 This function generates a vector of length \code{n} with values from 0 to 
 \code{folds-1} to mark train and test splits.
 }
+\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}.
+}
+\seealso{
+\code{\link{gensvm.grid}}
+}
+\author{
+Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
+Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
+}
diff --git a/man/gensvm.rank.score.Rd b/man/gensvm.rank.score.Rd
index a92d4be..627e60b 100644
--- a/man/gensvm.rank.score.Rd
+++ b/man/gensvm.rank.score.Rd
@@ -9,8 +9,11 @@ gensvm.rank.score(x)
 \arguments{
 \item{x}{array of numeric values}
 }
-\details{
-This function computes the ranks for the values in an array. The highest 
-value gets the smallest rank. Ties are broken by assigning the smallest 
-value.
+\value{
+array with the ranks of the values in the input array.
+}
+\description{
+This function computes the ranks for the values in an array. 
+The highest value gets the smallest rank. Ties are broken by assigning the 
+smallest value. The smallest rank is 1.
 }
diff --git a/man/gensvm.refit.Rd b/man/gensvm.refit.Rd
index 8a711bc..cae0646 100644
--- a/man/gensvm.refit.Rd
+++ b/man/gensvm.refit.Rd
@@ -4,7 +4,7 @@
 \alias{gensvm.refit}
 \title{Train an already fitted model on new data}
 \usage{
-gensvm.refit(fit, X, y, p = NULL, lambda = NULL, kappa = NULL,
+gensvm.refit(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)
@@ -12,16 +12,59 @@ gensvm.refit(fit, X, y, p = NULL, lambda = NULL, kappa = NULL,
 \arguments{
 \item{fit}{Fitted \code{gensvm} object}
 
-\item{X}{Data matrix of the new data}
+\item{x}{Data matrix of the new data}
 
 \item{y}{Label vector of the new data}
 
-\item{verbose}{Turn on verbose output and fit progress. If NULL (the 
-default) the value from the fitted model is chosen.}
+\item{p}{if NULL use the value from \code{fit} in the new model, otherwise 
+override with this value.}
+
+\item{lambda}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{kappa}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{epsilon}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{weights}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{kernel}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{gamma}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{coef}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{degree}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{kernel.eigen.cutoff}{if NULL use the value from \code{fit} in the new 
+model, otherwise override with this value.}
+
+\item{max.iter}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{verbose}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
+
+\item{random.seed}{if NULL use the value from \code{fit} in the new model, 
+otherwise override with this value.}
 }
 \value{
 a new fitted \code{gensvm} model
 }
+\description{
+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.
+}
 \examples{
 x <- iris[, -5]
 y <- iris[, 5]
diff --git a/man/gensvm.train.test.split.Rd b/man/gensvm.train.test.split.Rd
index fe2b1fb..261f7d1 100644
--- a/man/gensvm.train.test.split.Rd
+++ b/man/gensvm.train.test.split.Rd
@@ -27,6 +27,14 @@ of 0.25 will be used.}
 \item{shuffle}{shuffle the rows or not}
 
 \item{random.state}{seed for the random number generator (int)}
+
+\item{return.idx}{whether or not to return the indices in the output}
+}
+\value{
+a list with \code{x.train} and \code{x.test} splits of the \code{x} 
+array provided. If \code{y} is provided, also \code{y.train} and 
+\code{y.test}. If \code{return.idx} is TRUE, also \code{idx.train} and 
+\code{idx.test}.
 }
 \description{
 Often it is desirable to split a dataset into a training and 
diff --git a/man/gensvm.validate.param.grid.Rd b/man/gensvm.validate.param.grid.Rd
index 69882fe..7deacfd 100644
--- a/man/gensvm.validate.param.grid.Rd
+++ b/man/gensvm.validate.param.grid.Rd
@@ -6,4 +6,12 @@
 \usage{
 gensvm.validate.param.grid(df)
 }
+\value{
+TRUE if all values pass their respective conditions, FALSE 
+otherwise.
+}
+\description{
+Internal function to validate all parameters in a parameter 
+grid.
+}
 \keyword{internal}
diff --git a/man/gensvm.validate.params.Rd b/man/gensvm.validate.params.Rd
index 8e50137..ca6ddab 100644
--- a/man/gensvm.validate.params.Rd
+++ b/man/gensvm.validate.params.Rd
@@ -7,4 +7,12 @@
 gensvm.validate.params(p = NULL, kappa = NULL, lambda = NULL,
   epsilon = NULL, gamma = NULL, weights = NULL, kernel = NULL, ...)
 }
+\value{
+TRUE if all values pass their respective conditions, FALSE 
+otherwise.
+}
+\description{
+Internal function used to validate the parameters passed to the 
+gensvm() function.
+}
 \keyword{internal}
diff --git a/man/plot.gensvm.Rd b/man/plot.gensvm.Rd
index d99c2a0..6e19228 100644
--- a/man/plot.gensvm.Rd
+++ b/man/plot.gensvm.Rd
@@ -4,15 +4,18 @@
 \alias{plot.gensvm}
 \title{Plot the simplex space of the fitted GenSVM model}
 \usage{
-\method{plot}{gensvm}(fit, y, x.test = NULL, with.margins = TRUE,
+\method{plot}{gensvm}(x, labels, newdata = NULL, with.margins = TRUE,
   with.shading = TRUE, with.legend = TRUE, center.plot = TRUE,
   xlim = NULL, ylim = NULL, ...)
 }
 \arguments{
-\item{fit}{A fitted \code{gensvm} object}
+\item{x}{A fitted \code{gensvm} object}
 
-\item{y}{the labels to color points with (if NULL the predicted labels are 
-used)}
+\item{labels}{the labels to color points with. If this is omitted the 
+predicted labels are used.}
+
+\item{newdata}{the dataset to plot. If this is NULL the training data is 
+used.}
 
 \item{with.margins}{plot the margins}
 
@@ -31,16 +34,14 @@ bounds will be used for the vertical axis and the value of center.plot will
 be ignored}
 
 \item{...}{further arguments are passed to the builtin plot() function}
-
-\item{x}{the dataset to plot (if NULL the training data is used)}
 }
 \value{
 returns the object passed as input
 }
 \description{
 This function creates a plot of the simplex space for a fitted 
-GenSVM model and the given data set, as long as the dataset consists of only 
-3 classes.  For more than 3 classes, the simplex space is too high 
+GenSVM model and the given data set. This function works for dataset with 
+two or three classes. For more than 3 classes, the simplex space is too high 
 dimensional to easily visualize.
 }
 \examples{
@@ -59,8 +60,14 @@ plot(fit, y)
 # plot only misclassified samples
 x.mis <- x[predict(fit) != y, ]
 y.mis.true <- y[predict(fit) != y]
-plot(fit, x.test=x.mis)
-plot(fit, y.mis.true, x.test=x.mis)
+plot(fit, newdata=x.mis)
+plot(fit, y.mis.true, newdata=x.mis)
+
+# plot a 2-d model
+xx <- x[y \%in\% c('versicolor', 'virginica'), ]
+yy <- y[y \%in\% c('versicolor', 'virginica')]
+fit <- gensvm(xx, yy, kernel='rbf', max.iter=5000)
+plot(fit)
 
 }
 \references{
diff --git a/man/plot.gensvm.grid.Rd b/man/plot.gensvm.grid.Rd
index 44c599e..f23abda 100644
--- a/man/plot.gensvm.grid.Rd
+++ b/man/plot.gensvm.grid.Rd
@@ -4,10 +4,10 @@
 \alias{plot.gensvm.grid}
 \title{Plot the simplex space of the best fitted model in the GenSVMGrid}
 \usage{
-\method{plot}{gensvm.grid}(grid, ...)
+\method{plot}{gensvm.grid}(x, ...)
 }
 \arguments{
-\item{grid}{A \code{gensvm.grid} object trained with refit=TRUE}
+\item{x}{A \code{gensvm.grid} object trained with refit=TRUE}
 
 \item{...}{further arguments are passed to the plot function}
 }
diff --git a/man/predict.gensvm.Rd b/man/predict.gensvm.Rd
index 015623f..b521872 100644
--- a/man/predict.gensvm.Rd
+++ b/man/predict.gensvm.Rd
@@ -4,10 +4,10 @@
 \alias{predict.gensvm}
 \title{Predict class labels with the GenSVM model}
 \usage{
-\method{predict}{gensvm}(fit, newdata, add.rownames = FALSE, ...)
+\method{predict}{gensvm}(object, newdata, add.rownames = FALSE, ...)
 }
 \arguments{
-\item{fit}{Fitted \code{gensvm} object}
+\item{object}{Fitted \code{gensvm} object}
 
 \item{newdata}{Matrix of new data for which predictions need to be made.}
 
diff --git a/man/predict.gensvm.grid.Rd b/man/predict.gensvm.grid.Rd
index 01bf610..0c3cf2f 100644
--- a/man/predict.gensvm.grid.Rd
+++ b/man/predict.gensvm.grid.Rd
@@ -4,13 +4,13 @@
 \alias{predict.gensvm.grid}
 \title{Predict class labels from the GenSVMGrid class}
 \usage{
-\method{predict}{gensvm.grid}(grid, newx, ...)
+\method{predict}{gensvm.grid}(object, newdata, ...)
 }
 \arguments{
-\item{grid}{A \code{gensvm.grid} object trained with \code{refit=TRUE}}
+\item{object}{A \code{gensvm.grid} object trained with \code{refit=TRUE}}
 
-\item{newx}{Matrix of new values for \code{x} for which predictions need to 
-be computed.}
+\item{newdata}{Matrix of new values for \code{x} for which predictions need 
+to be computed.}
 
 \item{\dots}{further arguments are passed to predict.gensvm()}
 }
diff --git a/man/print.gensvm.Rd b/man/print.gensvm.Rd
index e3367fa..d8a7046 100644
--- a/man/print.gensvm.Rd
+++ b/man/print.gensvm.Rd
@@ -4,10 +4,10 @@
 \alias{print.gensvm}
 \title{Print the fitted GenSVM model}
 \usage{
-\method{print}{gensvm}(fit, ...)
+\method{print}{gensvm}(x, ...)
 }
 \arguments{
-\item{fit}{A \code{gensvm} object to print}
+\item{x}{A \code{gensvm} object to print}
 
 \item{\dots}{further arguments are ignored}
 }
diff --git a/man/print.gensvm.grid.Rd b/man/print.gensvm.grid.Rd
index 3b603c1..0a5377d 100644
--- a/man/print.gensvm.grid.Rd
+++ b/man/print.gensvm.grid.Rd
@@ -4,10 +4,10 @@
 \alias{print.gensvm.grid}
 \title{Print the fitted GenSVMGrid model}
 \usage{
-\method{print}{gensvm.grid}(grid, ...)
+\method{print}{gensvm.grid}(x, ...)
 }
 \arguments{
-\item{grid}{a \code{gensvm.grid} object to print}
+\item{x}{a \code{gensvm.grid} object to print}
 
 \item{\dots}{further arguments are ignored}
 }