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authorGertjan van den Burg <gertjanvandenburg@gmail.com>2018-03-27 13:57:25 +0100
committerGertjan van den Burg <gertjanvandenburg@gmail.com>2018-03-27 13:57:25 +0100
commit50b52b707ea3d02a26b42554a424c3983f16aa58 (patch)
tree625e0ac415a5e88743255f5b2a5e956f78dc169b /man
parentupdate submodule (diff)
downloadrgensvm-50b52b707ea3d02a26b42554a424c3983f16aa58.tar.gz
rgensvm-50b52b707ea3d02a26b42554a424c3983f16aa58.zip
fix examples after testing them all
Diffstat (limited to 'man')
-rw-r--r--man/gensvm.maxabs.scale.Rd1
-rw-r--r--man/gensvm.rank.score.Rd6
-rw-r--r--man/gensvm.refit.Rd2
-rw-r--r--man/plot.gensvm.Rd6
4 files changed, 4 insertions, 11 deletions
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{