removed dontrun from examples (issue #12)

R/data.R

@@ -38,9 +38,7 @@
 #' 
 #' @examples 
 #' data(truffles)
-#' \dontrun{
 #' summary(truffles)
 #' plot(1:15, rainfall[1, ], type = "l", xlab = "month", ylab = "rainfall (mm)")
-#' }
 
 NULL
\ No newline at end of file

R/ridgeSIR.R

@@ -46,7 +46,7 @@
 #' y <- log(abs(x %*% beta[ ,1])) + sqrt(abs(x %*% beta[ ,2]))
 #' y <- y + rnorm(50, sd = 0.1)
 #' res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
-#' \dontrun{print(res_ridge)}
+#' res_ridge
 #' 
 #' @return S3 object of class \code{ridgeRes}: a list consisting of
 #' \describe{
@@ -245,9 +245,9 @@ print.ridgeRes <- function(x, ...) {
 #' listH <- c(5, 10)
 #' list_d <- 1:4
 #' set.seed(1129)
-#' \dontrun{
-#' res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, 
-#'                           nfolds = 10, parallel = TRUE)}
+#' \donttest{
+#' res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, nfolds = 10, 
+#'                           parallel = TRUE, ncores = 2)}
 #' 
 #' @return a data frame with tested parameters and corresponding CV error and 
 #' estimation of R(d)
@@ -285,7 +285,7 @@ tune.ridgeSIR <- function(x, y, listH, list_mu2, list_d, nfolds = 10,
       mu2 <- list_mu2[ind_mu2]
       res_SIR <- processSIR(pre, max_d, mu2)
       global_projector[[ind_mu2]] <- sapply(list_d, function(d) {
-        projectorEDR(res_SIR$EDR[ ,1:d,drop = FALSE], pre$ccov_x, mu2)
+        projectorEDR(res_SIR$EDR[ , 1:d, drop = FALSE], pre$ccov_x, mu2)
       }, simplify = FALSE)
     }
     

R/sfcb_methods.R

@@ -66,11 +66,9 @@
 #'              summary.method = "pls", selection.method = "relief")
 #' summary(out1)
 #' 
-#' \dontrun{
 #' plot(out1)
 #' plot(out1, plot.type = "selection")
 #' plot(out1, plot.type = "importance")
-#' }
 #' 
 #' out2 <- sfcb(rainfall, truffles, group.method = "adjclust", 
 #'              summary.method = "basics", selection.method = "none",

R/sparseSIR.R

@@ -90,6 +90,7 @@ sparseSIR <- function(object, inter_len, adaptive = FALSE, sel_prop = 0.05,
                       parallel = FALSE, ncores = NULL) {
   if (parallel) {
     if (is.null(ncores)) ncores <- min(detectCores() - 1)
+    print(ncores)
     registerDoParallel(cores = ncores)
   }
   oldwarn <- getOption("warn")
@@ -259,7 +260,7 @@ print.sparseRes <- function(x, ...) {
 #' beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
 #' y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
 #' y <- y + rnorm(nsim, sd = 0.1)
-#' \dontrun{
+#' \donttest{
 #' res_ridge <- ridgeSIR(x, y, H = 10, d = 2)
 #' res_sparse <- sparseSIR(res_ridge, rep(1, ncol(x)))
 #' proj_data <- project(res_sparse)
@@ -334,7 +335,8 @@ project <- function(object) {
 #' y <- log(abs(x %*% beta[ ,1]) + 1) + sqrt(abs(x %*% beta[ ,2]))
 #' y <- y + rnorm(nsim, sd = 0.1)
 #' res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
-#' \dontrun{res_fused <- SISIR(res_ridge, rep(1, ncol(x)))}
+#' \donttest{res_fused <- SISIR(res_ridge, rep(1, ncol(x)), ncores = 2)
+#' res_fused}
 #' 
 #' @return S3 object of class \code{SISIR}: a list consisting of
 #' \describe{

man/SFCB-class.Rd

@@ -90,11 +90,9 @@ out1 <- sfcb(rainfall, truffles, group.method = "adjclust",
              summary.method = "pls", selection.method = "relief")
 summary(out1)
 
-\dontrun{
 plot(out1)
 plot(out1, plot.type = "selection")
 plot(out1, plot.type = "importance")
-}
 
 out2 <- sfcb(rainfall, truffles, group.method = "adjclust", 
              summary.method = "basics", selection.method = "none",

man/SISIR.Rd

@@ -74,7 +74,8 @@ beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
 y <- log(abs(x \%*\% beta[ ,1]) + 1) + sqrt(abs(x \%*\% beta[ ,2]))
 y <- y + rnorm(nsim, sd = 0.1)
 res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
-\dontrun{res_fused <- SISIR(res_ridge, rep(1, ncol(x)))}
+\donttest{res_fused <- SISIR(res_ridge, rep(1, ncol(x)), ncores = 2)
+res_fused}
 
 }
 \references{

man/project.Rd

@@ -36,7 +36,7 @@ beta[((tsteps < 0.2) | (tsteps > 0.5)), 1] <- 0
 beta[((tsteps < 0.6) | (tsteps > 0.75)), 2] <- 0
 y <- log(abs(x \%*\% beta[ ,1]) + 1) + sqrt(abs(x \%*\% beta[ ,2]))
 y <- y + rnorm(nsim, sd = 0.1)
-\dontrun{
+\donttest{
 res_ridge <- ridgeSIR(x, y, H = 10, d = 2)
 res_sparse <- sparseSIR(res_ridge, rep(1, ncol(x)))
 proj_data <- project(res_sparse)

man/ridgeSIR.Rd

@@ -56,7 +56,7 @@ beta <- cbind(sin(tsteps*3*pi/2), sin(tsteps*5*pi/2))
 y <- log(abs(x \%*\% beta[ ,1])) + sqrt(abs(x \%*\% beta[ ,2]))
 y <- y + rnorm(50, sd = 0.1)
 res_ridge <- ridgeSIR(x, y, H = 10, d = 2, mu2 = 10^8)
-\dontrun{print(res_ridge)}
+res_ridge
 
 }
 \references{

man/truffles.Rd

@@ -32,11 +32,9 @@ station) and truffle production data are courtesy of the truffle patch.
 }
 \examples{
 data(truffles)
-\dontrun{
 summary(truffles)
 plot(1:15, rainfall[1, ], type = "l", xlab = "month", ylab = "rainfall (mm)")
 }
-}
 \references{
 Baragatti M., Grollemund P.M., Montpied P., Dupouey J.L., Gravier J., Murat 
 C., Le Tacon F. (2019) Influence of annual climatic variations, climate

man/tune.ridgeSIR.Rd

@@ -55,9 +55,9 @@ list_mu2 <- 10^(0:10)
 listH <- c(5, 10)
 list_d <- 1:4
 set.seed(1129)
-\dontrun{
-res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, 
-                          nfolds = 10, parallel = TRUE)}
+\donttest{
+res_tune <- tune.ridgeSIR(x, y, listH, list_mu2, list_d, nfolds = 10, 
+                          parallel = TRUE, ncores = 2)}
 
 }
 \references{