similar to: nnet and weights: error analysis using V&R example

Dear R-users I use nnet for a classification (2 classes) problem. I use the code CVnn1, CVnn2 as described in V&R. The thing I changed to the code is: I define the (class) weight for each observation in each cv 'bag' and give the vector of weights as parameter of nnet(..weights = weight.vector...) Unfortunately I get an error during some (but not all!) inner-fold cv runs:

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Hi All, I am trying to use a neural network for my work, but I am not sure about my approach to select a parsimonious model. In R with nnet, the IAC has not been defined for a feed-forward neural network with a single hidden layer. Is this because it does not make sens mathematically in this case? For example, is this pseudo code sensible? Thanks in advance for your help. I am sorry if this

Hi, I am trying to determine the number of units in the hidden layer and the decay rate using the CVnn2 script found in MASS directory (reference: pg 348,MASS-4). The model that I am using is in the form of Y ~ X1 + X2 + X3... + X11 and the underlying data is time-series in nature. I found the MASS and nnet package extremely useful (many thanks to the contributors). However I am getting

> From: Peter Dalgaard BSA <p.dalgaard@biostat.ku.dk> > > Martin Maechler <maechler@stat.math.ethz.ch> writes: > > > Reading the long output of ``cc -flags'', > > I see that more than average optimization is done using > > cc -xO[1-4] > > # And -O is -xO2. If you do want higher speed, you need to use other flags too, and -fast is a

Dear listers, I have a piece of code which performs an ANOVA type of analysis on 2D GC data. The code is shown below: # ANOVA 2D GC analysis # maxc <- number of samples # nreps <- number of samples maxc <- 2 nreps <- 4 sscl <- NULL cmean <- NULL # # Initial stat. variable # dftot <- nrow(mat)-1 dfcl <- maxc - 1 dferr <- dftot - dfcl totmean

Using the data set fgl in MASS the following code layout(matrix(1:9,3,3)) for(i in 1:9){ boxplot(fgl[,i] ~ type, data = fgl,main=dimnames(fgl)[[2]][i])} produces a 3 by 3 array of plots, each one of which consists of six boxplots. Is it possible to do this in lattice? Steve "R version 2.7.2 (2008-08-25)" on Ubuntu 6.06

On 2020/10/30 ??3:45, Si-Wei Liu wrote: > This reverts commit 7ed9e3d97c32d969caded2dfb6e67c1a2cc5a0b1. > > Signed-off-by: Si-Wei Liu <si-wei.liu at oracle.com> > --- > drivers/vhost/vdpa.c | 119 +++++++++++++++++++++------------------------------ > 1 file changed, 48 insertions(+), 71 deletions(-) I saw this has been reverted there

Pinned pages are not properly accounted particularly when mapping error occurs on IOTLB update. Clean up dangling pinned pages for the error path. As the inflight pinned pages, specifically for memory region that strides across multiple chunks, would need more than one free page for book keeping and accounting. For simplicity, pin pages for all memory in the IOVA range in one go rather than have

Pinned pages are not properly accounted particularly when mapping error occurs on IOTLB update. Clean up dangling pinned pages for the error path. As the inflight pinned pages, specifically for memory region that strides across multiple chunks, would need more than one free page for book keeping and accounting. For simplicity, pin pages for all memory in the IOVA range in one go rather than have

Dear sir or ma'am, I have a question about the dataset "fgl." The dataset seems to be in the "VR" package, so I tried to download it from CRAN. However, after downloading, when I tried to load the package, it was not in my package list. I am wondering what is wrong. Any advice on how to access the fgl dataset would be appreciated. Thanks. Seungho Huh

Greetings: In R parallel's vignette, there is a comment "It would however take only slightly more work to allocate a stream to each task." (p.6). I've written down a working example that can allocate not just one, but several separate seeds for each task. (We have just a few project here that need multiple streams). I would like to help work that up for inclusion in the

Full_Name: Martha Nason Version: 2.0.1 OS: Windows XP Submission from: (NULL) (137.187.154.154) I am running simulations using fisher's test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c(

Recently, I was working with some lagged designs where a vector of observations at one time was used to predict a vector of observations at another time using a lag 1 design. In the work, I noticed a lot of negative correlations, so I ran a simple simulation with 2 matched points. The crude simulation example below shows that the correlation can be -1 or +1, but interestingly if you do this

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Dear R-devel, A user reported a strange problem with predict.randomForest in the randomForest package yesterday, and I'm baffled by it. The code at the end of the message produces the error. The problem is that, in predict.randomForest, there's a .Fortran call to "runforest". One of the arguments passed in is "countts", which is a vector of doubles. The error

Dear all, Objective: I am trying to learn about neural networks. I want to see if i can train an artificial neural network model to discriminate between spam and nonspam emails. Problem: I created my own model (example 1 below) and got an error of about 7.7%. I created the same model using the Rattle package (example 2 below, based on rattles log script) and got a much better error of about

On 11/30/2012 6:36 PM, Lang Hames wrote: > > > RBP is used as the frame pointer on x86 (hence its automatic > appearance in your code), and shouldn't be allocated to any vreg in > function bar. Loading/saving RBP should be managed by the stack frame > setup/teardown code. > If it doesn't already, your allocator should filter out reserved > registers (See

Versions of the V&R libraries, VR_5.3pl025-1.tar.gz, and of tree_0.2-2.tar.gz (giving most of the functionality of S's tree system including all that I have ever used) are now available on CRAN's Vienna node, and will propagate. There is an early draft of an R user's complements on Venables & Ripley available from http://www.stats.ox.ac.uk/pub/MASS2/Compl.shtml This is

Versions of the V&R libraries, VR_5.3pl025-1.tar.gz, and of tree_0.2-2.tar.gz (giving most of the functionality of S's tree system including all that I have ever used) are now available on CRAN's Vienna node, and will propagate. There is an early draft of an R user's complements on Venables & Ripley available from http://www.stats.ox.ac.uk/pub/MASS2/Compl.shtml This is