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authorGertjan van den Burg <gertjanvandenburg@gmail.com>2018-03-30 21:22:26 +0100
committerGertjan van den Burg <gertjanvandenburg@gmail.com>2018-03-30 21:22:26 +0100
commit93115020ec89c7f549ef5dab50e5270b09830894 (patch)
tree32d2af78bd670b196e9e51d07c0efd245aee69d6 /man
parentGet column names from x directly (diff)
downloadrgensvm-93115020ec89c7f549ef5dab50e5270b09830894.tar.gz
rgensvm-93115020ec89c7f549ef5dab50e5270b09830894.zip
Update docs and remove xtrain from output list
Diffstat (limited to 'man')
-rw-r--r--man/gensvm.Rd10
1 files changed, 5 insertions, 5 deletions
diff --git a/man/gensvm.Rd b/man/gensvm.Rd
index 7a55bd6..5aabcaa 100644
--- a/man/gensvm.Rd
+++ b/man/gensvm.Rd
@@ -4,13 +4,16 @@
\alias{gensvm}
\title{Fit the GenSVM model}
\usage{
-gensvm(X, y, p = 1, lambda = 1e-08, kappa = 0, epsilon = 1e-06,
+gensvm(x, y, p = 1, lambda = 1e-08, kappa = 0, epsilon = 1e-06,
weights = "unit", kernel = "linear", gamma = "auto", coef = 1,
degree = 2, kernel.eigen.cutoff = 1e-08, verbose = FALSE,
random.seed = NULL, max.iter = 1e+08, seed.V = NULL)
}
\arguments{
-\item{X}{data matrix with the predictors}
+\item{x}{data matrix with the predictors. \cr\cr
+Note that for SVMs categorical features should be converted to binary dummy
+features. This can be done with using the \code{\link{model.matrix}}
+function (i.e. \code{model.matrix( ~ var - 1)}).}
\item{y}{class labels}
@@ -77,9 +80,6 @@ eigendecomposition of the kernel matrix.}
\item{n.iter}{Number of iterations performed in training}
\item{n.support}{Number of support vectors in the final model}
\item{training.time}{Total training time}
-\item{X.train}{When training with nonlinear kernels, the training data is
-needed to perform prediction. For these kernels it is therefore stored in
-the fitted model.}
}
\description{
Fits the Generalized Multiclass Support Vector Machine model