fix examples after testing them all

8 files changed, 8 insertions, 21 deletions

diff --git a/R/gensvm.grid.R b/R/gensvm.grid.R
index 5d27fde..2b90e57 100644
--- a/R/gensvm.grid.R
+++ b/R/gensvm.grid.R
@@ -592,11 +592,6 @@ gensvm.expand.param.grid <- function(pg, n.features)
 #'
 #' @param x array of numeric values
 #'
-#' @examples
-#' x <- c(7, 0.1, 0.5, 0.1, 10, 100, 200)
-#' gensvm.rank.score(x)
-#' [ 4 6 5 6 3 2 1 ]
-#'
 gensvm.rank.score <- function(x)
 {
     x <- as.array(x)
diff --git a/R/gensvm.maxabs.scale.R b/R/gensvm.maxabs.scale.R
index 6ac351b..42b0be2 100644
--- a/R/gensvm.maxabs.scale.R
+++ b/R/gensvm.maxabs.scale.R
@@ -44,7 +44,6 @@
 #' apply(x.scale, 2, max)
 #'
 #' # with a train and test dataset
-#' x <- iris[, -5]
 #' split <- gensvm.train.test.split(x)
 #' x.train <- split$x.train
 #' x.test <- split$x.test
diff --git a/R/gensvm.refit.R b/R/gensvm.refit.R
index a6af3fd..fab7579 100644
--- a/R/gensvm.refit.R
+++ b/R/gensvm.refit.R
@@ -31,7 +31,7 @@
 #'
 #' # fit a standard model and refit with slightly different parameters
 #' fit <- gensvm(x, y)
-#' fit2 <- gensvm.refit(x, y, epsilon=1e-8)
+#' fit2 <- gensvm.refit(fit, x, y, epsilon=1e-8)
 #'
 #' # refit a model returned by a grid search
 #' grid <- gensvm.grid(x, y)
diff --git a/R/plot.gensvm.R b/R/plot.gensvm.R
index 0ce215b..85ff3b5 100644
--- a/R/plot.gensvm.R
+++ b/R/plot.gensvm.R
@@ -46,9 +46,9 @@
 #'
 #' # plot only misclassified samples
 #' x.mis <- x[predict(fit, x) != y, ]
-#' y.mis.true <- y[predict(fit, x) != y, ]
-#' plot(fit, x.bad)
-#' plot(fit, x.bad, y.true=y.mis.true)
+#' y.mis.true <- y[predict(fit, x) != y]
+#' plot(fit, x.mis)
+#' plot(fit, x.mis, y.true=y.mis.true)
 #'
 plot.gensvm <- function(fit, x, y.true=NULL, with.margins=TRUE, 
                         with.shading=TRUE, with.legend=TRUE, center.plot=TRUE,
diff --git a/man/gensvm.maxabs.scale.Rd b/man/gensvm.maxabs.scale.Rd
index 50c6413..0bb09ba 100644
--- a/man/gensvm.maxabs.scale.Rd
+++ b/man/gensvm.maxabs.scale.Rd
@@ -43,7 +43,6 @@ apply(x.scale, 2, min)
 apply(x.scale, 2, max)
 
 # with a train and test dataset
-x <- iris[, -5]
 split <- gensvm.train.test.split(x)
 x.train <- split$x.train
 x.test <- split$x.test
diff --git a/man/gensvm.rank.score.Rd b/man/gensvm.rank.score.Rd
index 21d6bcd..3c1549d 100644
--- a/man/gensvm.rank.score.Rd
+++ b/man/gensvm.rank.score.Rd
@@ -14,10 +14,4 @@ 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.
 }
-\examples{
-x <- c(7, 0.1, 0.5, 0.1, 10, 100, 200)
-gensvm.rank.score(x)
-[ 4 6 5 6 3 2 1 ]
-
-}
 
diff --git a/man/gensvm.refit.Rd b/man/gensvm.refit.Rd
index 194cde3..18803af 100644
--- a/man/gensvm.refit.Rd
+++ b/man/gensvm.refit.Rd
@@ -28,7 +28,7 @@ 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)
+fit2 <- gensvm.refit(fit, x, y, epsilon=1e-8)
 
 # refit a model returned by a grid search
 grid <- gensvm.grid(x, y)
diff --git a/man/plot.gensvm.Rd b/man/plot.gensvm.Rd
index b597e18..e28a108 100644
--- a/man/plot.gensvm.Rd
+++ b/man/plot.gensvm.Rd
@@ -51,9 +51,9 @@ plot(fit, x, y.true=y)
 
 # plot only misclassified samples
 x.mis <- x[predict(fit, x) != y, ]
-y.mis.true <- y[predict(fit, x) != y, ]
-plot(fit, x.bad)
-plot(fit, x.bad, y.true=y.mis.true)
+y.mis.true <- y[predict(fit, x) != y]
+plot(fit, x.mis)
+plot(fit, x.mis, y.true=y.mis.true)
 
 }
 \author{