fixes with submission - namespace is correct, examples are shorter

NAMESPACE

@@ -41,3 +41,16 @@ import(nnet)
 import(reshape2)
 import(scales)
 import(tidyr)
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(graphics,points)
+importFrom(graphics,segments)
+importFrom(graphics,text)
+importFrom(stats,coef)
+importFrom(stats,formula)
+importFrom(stats,kmeans)
+importFrom(stats,model.frame)
+importFrom(stats,predict)
+importFrom(stats,quantile)
+importFrom(stats,terms)
+importFrom(utils,capture.output)

R/NeuralNetTools_gar.R

@@ -56,6 +56,7 @@
 #'  
 #' garson(mod)  
 #' 
+#' \dontrun{
 #' ## using RSNNS, no bias layers
 #' 
 #' library(RSNNS)
@@ -76,15 +77,12 @@
 #' 
 #' ## using caret
 #' 
-#' \dontrun{
 #' library(caret)
 #' 
 #' mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 #' 
 #' garson(mod)
 #' 
-#' }
-#' 
 #' ## modify the plot using ggplot2 syntax
 #' library(ggplot2)
 #' 
@@ -95,6 +93,7 @@
 #'   scale_y_continuous('Rel. Importance', limits = c(-1, 1)) + 
 #'   scale_fill_gradientn(colours = cols) + 
 #'   scale_colour_gradientn(colours = cols)
+#'}
 garson <- function(mod_in, ...) UseMethod('garson')
 
 #' @rdname garson

R/NeuralNetTools_lek.R

@@ -48,6 +48,7 @@
 #'  
 #' lekprofile(mod)  
 #' 
+#' \dontrun{
 #' ## using RSNNS, no bias layers
 #' 
 #' library(RSNNS)
@@ -76,15 +77,12 @@
 #' 
 #' ## using caret
 #' 
-#' \dontrun{
 #' library(caret)
 #' 
 #' mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 #' 
 #' lekprofile(mod)
 #' 
-#' }
-#' 
 #' ## group by clusters instead of sequencing by quantiles
 #' 
 #' mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5)
@@ -98,6 +96,7 @@
 #' names(group_vals) <- names(group_vals)
 #' 
 #' lekprofile(mod, group_vals = group_vals, xsel = 'X3')
+#' }
 lekprofile <- function(mod_in, ...) UseMethod('lekprofile')
 
 #' @rdname lekprofile

R/NeuralNetTools_old.R

@@ -55,6 +55,7 @@
 #'  
 #' olden(mod)  
 #' 
+#' \dontrun{
 #' ## View the difference for a model w/ skip layers
 #' 
 #' set.seed(123)
@@ -82,23 +83,21 @@
 #' olden(mod)
 #' 
 #' ## using caret
-#' 
-#' \dontrun{
+#'
 #' library(caret)
 #'
 #' mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 #' 
 #' olden(mod)
 #' 
-#' }
-#' 
 #' ## multiple hidden layers
 #'
 #' x <- neuraldat[, c('X1', 'X2', 'X3')]
 #' y <- neuraldat[, 'Y1']
 #' mod <- mlp(x, y, size = c(5, 7, 6), linOut = TRUE)
 #' 
 #' olden(mod)
+#' }
 olden <- function(mod_in, ...) UseMethod('olden')
 
 #' @rdname olden

R/NeuralNetTools_plot.R

@@ -65,6 +65,7 @@
 #'
 #' plotnet(mod, skip = TRUE)  
 #' 
+#' \dontrun{
 #' ## using RSNNS, no bias layers
 #' 
 #' library(RSNNS)
@@ -75,7 +76,6 @@
 #' 
 #' plotnet(mod)
 #'
-#' \dontrun{
 #' # pruned model using code from RSSNS pruning demo
 #' pruneFuncParams <- list(max_pr_error_increase = 10.0, pr_accepted_error = 1.0, 
 #'  no_of_pr_retrain_cycles = 1000, min_error_to_stop = 0.01, init_matrix_value = 1e-6, 
@@ -85,7 +85,6 @@
 #' 
 #' plotnet(mod)
 #' plotnet(mod, prune_col = 'lightblue')
-#' }
 #' 
 #' ## using neuralnet
 #' 
@@ -97,13 +96,11 @@
 #' 
 #' ## using caret
 #'
-#' \dontrun{
 #' library(caret)
 #' 
 #' mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 #' 
 #' plotnet(mod)
-#' }
 #' 
 #' ## a more complicated network with categorical response
 #' AND <- c(rep(0, 7), 1)
@@ -133,6 +130,7 @@
 #' cols <- colorRampPalette(c('lightgreen', 'darkgreen'))(3)[rank(rel_imp)]
 #'  
 #' plotnet(mod, circle_col = list(cols, 'lightblue'))
+#' }
 plotnet <- function(mod_in, ...) UseMethod('plotnet')
 
 #' @rdname plotnet

R/NeuralNetTools_utils.R

@@ -33,6 +33,7 @@
 #'  
 #' neuralweights(mod)  
 #' 
+#' \dontrun{
 #' ## using RSNNS, no bias layers
 #' 
 #' library(RSNNS)
@@ -43,7 +44,6 @@
 #' 
 #' neuralweights(mod)
 #' 
-#' \dontrun{
 #' # pruned model using code from RSSNS pruning demo
 #' pruneFuncParams <- list(max_pr_error_increase = 10.0, pr_accepted_error = 1.0, 
 #'  no_of_pr_retrain_cycles = 1000, min_error_to_stop = 0.01, init_matrix_value = 1e-6, 
@@ -53,15 +53,14 @@
 #' 
 #' neuralweights(mod)
 #' 
-#' }
-#' 
 #' ## using neuralnet
 #' 
 #' library(neuralnet)
 #' 
 #' mod <- neuralnet(Y1 ~ X1 + X2 + X3, data = neuraldat, hidden = 5)
 #' 
 #' neuralweights(mod)
+#' }
 neuralweights <-  function(mod_in, ...) UseMethod('neuralweights')
 
 #' @rdname neuralweights
@@ -724,3 +723,8 @@ lekgrps <- function(grps){
   return(p)
 
 }
+
+#' @importFrom graphics par plot points segments text
+#' @importFrom stats coef formula kmeans model.frame predict quantile terms
+#' @importFrom utils capture.output
+NULL

cran-comments.md

@@ -1,3 +1,22 @@
+## Resubmission
+This is a resubmission.  In this version I have removed the NOTE by adding the following to the NAMESPACE: 
+
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(graphics,points)
+importFrom(graphics,segments)
+importFrom(graphics,text)
+importFrom(stats,coef)
+importFrom(stats,formula)
+importFrom(stats,kmeans)
+importFrom(stats,model.frame)
+importFrom(stats,predict)
+importFrom(stats,quantile)
+importFrom(stats,terms)
+importFrom(utils,capture.output)
+
+I have also reduced all examples in the documentation to run in less than five seconds.  
+
 ## Test environments
 * local Windows 7 install, R 3.2.2 
 * local Windows 7 install, Current r-devel (2015-11-30 r69717)

man/garson.Rd

@@ -76,6 +76,7 @@ mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5)
 
 garson(mod)
 
+\dontrun{
 ## using RSNNS, no bias layers
 
 library(RSNNS)
@@ -96,15 +97,12 @@ garson(mod)
 
 ## using caret
 
-\dontrun{
 library(caret)
 
 mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 
 garson(mod)
 
-}
-
 ## modify the plot using ggplot2 syntax
 library(ggplot2)
 
@@ -116,6 +114,7 @@ garson(mod) +
   scale_fill_gradientn(colours = cols) +
   scale_colour_gradientn(colours = cols)
 }
+}
 \references{
 Garson, G.D. 1991. Interpreting neural network connection weights. Artificial Intelligence Expert. 6(4):46-51.
 

man/lekprofile.Rd

@@ -70,6 +70,7 @@ mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5)
 
 lekprofile(mod)
 
+\dontrun{
 ## using RSNNS, no bias layers
 
 library(RSNNS)
@@ -98,15 +99,12 @@ lekprofile(mod)
 
 ## using caret
 
-\dontrun{
 library(caret)
 
 mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 
 lekprofile(mod)
 
-}
-
 ## group by clusters instead of sequencing by quantiles
 
 mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5)
@@ -121,6 +119,7 @@ names(group_vals) <- names(group_vals)
 
 lekprofile(mod, group_vals = group_vals, xsel = 'X3')
 }
+}
 \references{
 Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S. 1996. Application of neural networks to modelling nonlinear relationships in Ecology. Ecological Modelling. 90:39-52.
 

man/neuralweights.Rd

@@ -57,6 +57,7 @@ mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5, linout = TRUE)
 
 neuralweights(mod)
 
+\dontrun{
 ## using RSNNS, no bias layers
 
 library(RSNNS)
@@ -67,7 +68,6 @@ mod <- mlp(x, y, size = 5, linOut = TRUE)
 
 neuralweights(mod)
 
-\dontrun{
 # pruned model using code from RSSNS pruning demo
 pruneFuncParams <- list(max_pr_error_increase = 10.0, pr_accepted_error = 1.0,
  no_of_pr_retrain_cycles = 1000, min_error_to_stop = 0.01, init_matrix_value = 1e-6,
@@ -77,8 +77,6 @@ mod <- mlp(x, y, size = 5, pruneFunc = "OptimalBrainSurgeon",
 
 neuralweights(mod)
 
-}
-
 ## using neuralnet
 
 library(neuralnet)
@@ -87,4 +85,5 @@ mod <- neuralnet(Y1 ~ X1 + X2 + X3, data = neuraldat, hidden = 5)
 
 neuralweights(mod)
 }
+}
 

man/olden.Rd

@@ -78,6 +78,7 @@ mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5)
 
 olden(mod)
 
+\dontrun{
 ## View the difference for a model w/ skip layers
 
 set.seed(123)
@@ -106,15 +107,12 @@ olden(mod)
 
 ## using caret
 
-\dontrun{
 library(caret)
 
 mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 
 olden(mod)
 
-}
-
 ## multiple hidden layers
 
 x <- neuraldat[, c('X1', 'X2', 'X3')]
@@ -123,6 +121,7 @@ mod <- mlp(x, y, size = c(5, 7, 6), linOut = TRUE)
 
 olden(mod)
 }
+}
 \references{
 Garson, G.D. 1991. Interpreting neural network connection weights. Artificial Intelligence Expert. 6(4):46-51.
 

man/plotnet.Rd

@@ -119,6 +119,7 @@ mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5, skip = TRUE)
 
 plotnet(mod, skip = TRUE)
 
+\dontrun{
 ## using RSNNS, no bias layers
 
 library(RSNNS)
@@ -129,7 +130,6 @@ mod <- mlp(x, y, size = 5)
 
 plotnet(mod)
 
-\dontrun{
 # pruned model using code from RSSNS pruning demo
 pruneFuncParams <- list(max_pr_error_increase = 10.0, pr_accepted_error = 1.0,
  no_of_pr_retrain_cycles = 1000, min_error_to_stop = 0.01, init_matrix_value = 1e-6,
@@ -139,7 +139,6 @@ mod <- mlp(x, y, size = 5, pruneFunc = "OptimalBrainSurgeon",
 
 plotnet(mod)
 plotnet(mod, prune_col = 'lightblue')
-}
 
 ## using neuralnet
 
@@ -151,13 +150,11 @@ plotnet(mod)
 
 ## using caret
 
-\dontrun{
 library(caret)
 
 mod <- train(Y1 ~ X1 + X2 + X3, method = 'nnet', data = neuraldat, linout = TRUE)
 
 plotnet(mod)
-}
 
 ## a more complicated network with categorical response
 AND <- c(rep(0, 7), 1)
@@ -188,6 +185,7 @@ cols <- colorRampPalette(c('lightgreen', 'darkgreen'))(3)[rank(rel_imp)]
 
 plotnet(mod, circle_col = list(cols, 'lightblue'))
 }
+}
 \references{
 Ozesmi, S.L., Ozesmi, U. 1999. An artificial neural network approach to spatial habitat modeling with interspecific interaction. Ecological Modelling. 116:15-31.
 }