don't run a bunch of examples to make check faster

13 files changed, 30 insertions, 2 deletions

diff --git a/R/coef.gensvm.grid.R b/R/coef.gensvm.grid.R
index 48b2a76..f9908ba 100644
--- a/R/coef.gensvm.grid.R
+++ b/R/coef.gensvm.grid.R
@@ -26,11 +26,13 @@
 #' @importFrom stats coef
 #'
 #' @examples
+#' \dontrun{
 #' x <- iris[, -5]
 #' y <- iris[, 5]
 #'
 #' grid <- gensvm.grid(x, y)
 #' pg <- coef(grid)
+#' }
 #'
 coef.gensvm.grid <- function(object, ...)
 {
diff --git a/R/gensvm.R b/R/gensvm.R
index c541b5a..86a15c1 100644
--- a/R/gensvm.R
+++ b/R/gensvm.R
@@ -107,8 +107,8 @@
 #' fit <- gensvm(x, y, max.iter=1000)
 #'
 #' # Nonlinear training
-#' fit <- gensvm(x, y, kernel='rbf', max.iter=5000)
-#' fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=5000)
+#' fit <- gensvm(x, y, kernel='rbf', max.iter=1000)
+#' fit <- gensvm(x, y, kernel='poly', degree=2, gamma=1.0, max.iter=1000)
 #'
 #' # Setting the random seed and comparing results
 #' fit <- gensvm(x, y, random.seed=123)
diff --git a/R/gensvm.grid.R b/R/gensvm.grid.R
index 2bf673e..8fa187e 100644
--- a/R/gensvm.grid.R
+++ b/R/gensvm.grid.R
@@ -130,8 +130,10 @@
 #' x <- iris[, -5]
 #' y <- iris[, 5]
 #'
+#' \dontrun{
 #' # use the default parameter grid
 #' grid <- gensvm.grid(x, y, verbose=TRUE)
+#' }
 #'
 #' # use a smaller parameter grid
 #' pg <- expand.grid(p=c(1.0, 1.5, 2.0), kappa=c(-0.9, 1.0), epsilon=c(1e-3))
@@ -140,6 +142,7 @@
 #' # print the result
 #' print(grid)
 #'
+#' \dontrun{
 #' # Using a custom scoring function (accuracy as percentage)
 #' acc.pct <- function(yt, yp) { return (100 * sum(yt == yp) / length(yt)) }
 #' grid <- gensvm.grid(x, y, scoring=acc.pct)
@@ -148,6 +151,7 @@
 #' pg <- expand.grid(kernel=c('rbf'), gamma=c(1e-2, 1e-1, 1, 1e1, 1e2),
 #'                   lambda=c(1e-8, 1e-6), max.iter=c(5000))
 #' grid <- gensvm.grid(x, y, param.grid=pg, verbose=2)
+#' }
 #'
 gensvm.grid <- function(x, y, param.grid='tiny', refit=TRUE, scoring=NULL, cv=3, 
                         verbose=0, return.train.score=TRUE)
diff --git a/R/gensvm.refit.R b/R/gensvm.refit.R
index f3b818b..54040f4 100644
--- a/R/gensvm.refit.R
+++ b/R/gensvm.refit.R
@@ -60,9 +60,11 @@
 #' fit <- gensvm(x, y)
 #' fit2 <- gensvm.refit(fit, x, y, epsilon=1e-8)
 #'
+#' \dontrun{
 #' # refit a model returned by a grid search
 #' grid <- gensvm.grid(x, y)
 #' fit <- gensvm.refit(fit, x, y, epsilon=1e-8)
+#' }
 #'
 #' # refit on different data
 #' idx <- runif(nrow(x)) > 0.5
diff --git a/R/plot.gensvm.grid.R b/R/plot.gensvm.grid.R
index 6a34024..c755c3c 100644
--- a/R/plot.gensvm.grid.R
+++ b/R/plot.gensvm.grid.R
@@ -25,11 +25,13 @@
 #' @export
 #'
 #' @examples
+#' \dontrun{
 #' x <- iris[, -5]
 #' y <- iris[, 5]
 #'
 #' grid <- gensvm.grid(x, y)
 #' plot(grid, x)
+#' }
 #'
 plot.gensvm.grid <- function(x, ...)
 {
diff --git a/R/predict.gensvm.grid.R b/R/predict.gensvm.grid.R
index acc838f..d6f5e06 100644
--- a/R/predict.gensvm.grid.R
+++ b/R/predict.gensvm.grid.R
@@ -35,6 +35,7 @@
 #' @importFrom stats predict
 #'
 #' @examples
+#' \dontrun{
 #' x <- iris[, -5]
 #' y <- iris[, 5]
 #'
@@ -43,6 +44,7 @@
 #'
 #' # predict training sample
 #' y.hat <- predict(grid, x)
+#' }
 #'
 predict.gensvm.grid <- function(object, newdata, ...)
 {
diff --git a/R/print.gensvm.grid.R b/R/print.gensvm.grid.R
index 3e1bb69..d0755ca 100644
--- a/R/print.gensvm.grid.R
+++ b/R/print.gensvm.grid.R
@@ -25,12 +25,14 @@
 #' @export
 #'
 #' @examples
+#' \dontrun{
 #' x <- iris[, -5]
 #' y <- iris[, 5]
 #'
 #' # fit a grid search and print the resulting object
 #' grid <- gensvm.grid(x, y)
 #' print(grid)
+#' }
 #'
 print.gensvm.grid <- function(x, ...)
 {
diff --git a/man/coef.gensvm.grid.Rd b/man/coef.gensvm.grid.Rd
index 744809f..48cf48d 100644
--- a/man/coef.gensvm.grid.Rd
+++ b/man/coef.gensvm.grid.Rd
@@ -18,11 +18,13 @@ The parameter grid of the GenSVMGrid object as a data frame.
 Returns the parameter grid of a \code{gensvm.grid} object.
 }
 \examples{
+\dontrun{
 x <- iris[, -5]
 y <- iris[, 5]
 
 grid <- gensvm.grid(x, y)
 pg <- coef(grid)
+}
 
 }
 \references{
diff --git a/man/gensvm.grid.Rd b/man/gensvm.grid.Rd
index 147c044..a19b631 100644
--- a/man/gensvm.grid.Rd
+++ b/man/gensvm.grid.Rd
@@ -131,8 +131,10 @@ be the same as the param.grid in the input.
 x <- iris[, -5]
 y <- iris[, 5]
 
+\dontrun{
 # use the default parameter grid
 grid <- gensvm.grid(x, y, verbose=TRUE)
+}
 
 # use a smaller parameter grid
 pg <- expand.grid(p=c(1.0, 1.5, 2.0), kappa=c(-0.9, 1.0), epsilon=c(1e-3))
@@ -141,6 +143,7 @@ grid <- gensvm.grid(x, y, param.grid=pg)
 # print the result
 print(grid)
 
+\dontrun{
 # Using a custom scoring function (accuracy as percentage)
 acc.pct <- function(yt, yp) { return (100 * sum(yt == yp) / length(yt)) }
 grid <- gensvm.grid(x, y, scoring=acc.pct)
@@ -149,6 +152,7 @@ grid <- gensvm.grid(x, y, scoring=acc.pct)
 pg <- expand.grid(kernel=c('rbf'), gamma=c(1e-2, 1e-1, 1, 1e1, 1e2),
                   lambda=c(1e-8, 1e-6), max.iter=c(5000))
 grid <- gensvm.grid(x, y, param.grid=pg, verbose=2)
+}
 
 }
 \references{
diff --git a/man/gensvm.refit.Rd b/man/gensvm.refit.Rd
index cae0646..aa260ca 100644
--- a/man/gensvm.refit.Rd
+++ b/man/gensvm.refit.Rd
@@ -73,9 +73,11 @@ y <- iris[, 5]
 fit <- gensvm(x, y)
 fit2 <- gensvm.refit(fit, x, y, epsilon=1e-8)
 
+\dontrun{
 # refit a model returned by a grid search
 grid <- gensvm.grid(x, y)
 fit <- gensvm.refit(fit, x, y, epsilon=1e-8)
+}
 
 # refit on different data
 idx <- runif(nrow(x)) > 0.5
diff --git a/man/plot.gensvm.grid.Rd b/man/plot.gensvm.grid.Rd
index f23abda..a3aa4d9 100644
--- a/man/plot.gensvm.grid.Rd
+++ b/man/plot.gensvm.grid.Rd
@@ -20,11 +20,13 @@ model in the provided GenSVMGrid object. See the documentation for
 \code{\link{plot.gensvm}} for more information.
 }
 \examples{
+\dontrun{
 x <- iris[, -5]
 y <- iris[, 5]
 
 grid <- gensvm.grid(x, y)
 plot(grid, x)
+}
 
 }
 \references{
diff --git a/man/predict.gensvm.grid.Rd b/man/predict.gensvm.grid.Rd
index 0c3cf2f..a61362e 100644
--- a/man/predict.gensvm.grid.Rd
+++ b/man/predict.gensvm.grid.Rd
@@ -27,6 +27,7 @@ this model is only available if \code{refit=TRUE} was specified in the
 \code{\link{gensvm.grid}} call (the default).
 }
 \examples{
+\dontrun{
 x <- iris[, -5]
 y <- iris[, 5]
 
@@ -35,6 +36,7 @@ grid <- gensvm.grid(x, y)
 
 # predict training sample
 y.hat <- predict(grid, x)
+}
 
 }
 \references{
diff --git a/man/print.gensvm.grid.Rd b/man/print.gensvm.grid.Rd
index 0a5377d..1f19737 100644
--- a/man/print.gensvm.grid.Rd
+++ b/man/print.gensvm.grid.Rd
@@ -18,12 +18,14 @@ returns the object passed as input
 Prints the summary of the fitted GenSVMGrid model
 }
 \examples{
+\dontrun{
 x <- iris[, -5]
 y <- iris[, 5]
 
 # fit a grid search and print the resulting object
 grid <- gensvm.grid(x, y)
 print(grid)
+}
 
 }
 \references{