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authorGertjan van den Burg <gertjanvandenburg@gmail.com>2018-04-05 17:06:40 -0400
committerGertjan van den Burg <gertjanvandenburg@gmail.com>2018-04-05 17:07:51 -0400
commit93f34bdd35732578cfe2a8d14562df57669b47ea (patch)
tree32f3d0b960c7e4ede2f21bd739456409f6a1c3f5 /R
parentadd cran comments to git (diff)
downloadrgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.tar.gz
rgensvm-93f34bdd35732578cfe2a8d14562df57669b47ea.zip
fix spelling
Diffstat (limited to 'R')
-rw-r--r--R/gensvm.grid.R4
-rw-r--r--R/gensvm.maxabs.scale.R2
2 files changed, 3 insertions, 3 deletions
diff --git a/R/gensvm.grid.R b/R/gensvm.grid.R
index 8fa187e..599b588 100644
--- a/R/gensvm.grid.R
+++ b/R/gensvm.grid.R
@@ -8,7 +8,7 @@
#'
#' @param x training data matrix. We denote the size of this matrix by
#' n_samples x n_features.
-#' @param y training vector of class labes of length n_samples. The number of
+#' @param y training vector of class labels of length n_samples. The number of
#' unique labels in this vector is denoted by n_classes.
#' @param param.grid String (\code{'tiny'}, \code{'small'}, or \code{'full'})
#' or data frame with parameter configurations to evaluate. Typically this is
@@ -48,7 +48,7 @@
#' \item{cv.idx}{Array with cross validation indices used to split the data}
#'
#' @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/R/gensvm.maxabs.scale.R b/R/gensvm.maxabs.scale.R
index 47a6340..0c0f6f3 100644
--- a/R/gensvm.maxabs.scale.R
+++ b/R/gensvm.maxabs.scale.R
@@ -1,6 +1,6 @@
#' @title Scale each column of a matrix by its maximum absolute value
#'
-#' @description Scaling a dataset can creatly decrease the computation time of
+#' @description 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].