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| author | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-05 17:06:40 -0400 |
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| committer | Gertjan van den Burg <gertjanvandenburg@gmail.com> | 2018-04-05 17:07:51 -0400 |
| commit | 93f34bdd35732578cfe2a8d14562df57669b47ea (patch) | |
| tree | 32f3d0b960c7e4ede2f21bd739456409f6a1c3f5 /man | |
| parent | add cran comments to git (diff) | |
| download | rgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.tar.gz rgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.zip | |
fix spelling
Diffstat (limited to 'man')
| -rw-r--r-- | man/gensvm.grid.Rd | 4 | ||||
| -rw-r--r-- | man/gensvm.maxabs.scale.Rd | 2 |
2 files changed, 3 insertions, 3 deletions
diff --git a/man/gensvm.grid.Rd b/man/gensvm.grid.Rd index a19b631..94cdcd5 100644 --- a/man/gensvm.grid.Rd +++ b/man/gensvm.grid.Rd @@ -11,7 +11,7 @@ gensvm.grid(x, y, param.grid = "tiny", refit = TRUE, scoring = NULL, \item{x}{training data matrix. We denote the size of this matrix by n_samples x n_features.} -\item{y}{training vector of class labes of length n_samples. The number of +\item{y}{training vector of class labels of length n_samples. The number of unique labels in this vector is denoted by n_classes.} \item{param.grid}{String (\code{'tiny'}, \code{'small'}, or \code{'full'}) @@ -70,7 +70,7 @@ the user. } \section{Using a Parameter Grid}{ -To evaluate certain paramater configurations, a data frame can be supplied +To evaluate certain parameter configurations, a data frame can be supplied to the \code{param.grid} argument of the function. Such a data frame can easily be generated using the R function \code{expand.grid}, or could be created through other ways to test specific parameter configurations. diff --git a/man/gensvm.maxabs.scale.Rd b/man/gensvm.maxabs.scale.Rd index cbbfd29..7c75eff 100644 --- a/man/gensvm.maxabs.scale.Rd +++ b/man/gensvm.maxabs.scale.Rd @@ -18,7 +18,7 @@ is supplied, a list with elements \code{x} and \code{x.test} representing the scaled datasets. } \description{ -Scaling a dataset can creatly decrease the computation time of +Scaling a dataset can greatly decrease the computation time of GenSVM. This function scales the data by dividing each column of a matrix by the maximum absolute value of that column. This preserves sparsity in the data while mapping each column to the interval [-1, 1]. |