Displaying 20 results from an estimated 4000 matches similar to: "find the important inputs to the neural network model in "nnet" package"
2009 May 05
2
calibration plot
Hi,
I have a binary variable and corresponding predicted probability (using
logistic regression on some explanatoey variables);
I want to check that the model is well-calibrated using a calibration plot.
how can I have the calibration plot for my data?
thanks.
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2012 Sep 21
0
using neural network in R (nnet)
Hi all,
I have considered neural network to classify the health status of the cow.
I found a very neatly written R codes for classification method in
here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R>
.
It would be very helpful if you can answer some of the questions, that I am
struggling with,
I have set of time series data from different animals, I use
2011 May 11
0
Init nnetTs (or nnet?) with a former Neural Net
I am new to R and use nnetTs - calls.
If a time series of let's say 80000 Datapoints and did call nnetTs I want
make a new net
for the old ponts plus the next 1000 points (81000 datapoints total) what
would again
cost much calculation time.
So I want to pre-init the new net with the former wonnen net to reduce the
necessary
iteration numbers.
Is thee a possibility to do that and how?
i.e.:
2010 Jun 17
0
help with neural network nnet package
HI, Dear R community,
I am using the nnet to fit a neural network model to do classification on
binary target variable (0, 1). I am using the following codes:
nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=5, rang=0.3,
decay=5e-4, maxit=500)
I want to know what is the activation function for the original inputs, is
it sigmoid activation function?
and what is the output activation
2006 Nov 30
0
Preventing early stopping in neural network (nnet package)
Hello there,
I'm back again with another question about the neural network
package. I'm having trouble getting the network to run for the
maximum number of iterations. It always stops early, usually after
100 iterations claiming to have converged at an answer.
Now, for my purposes I want it to run for the entire number of
epochs, and I'm been looking at modifying the abstol
2013 Mar 27
0
A simple perceptron neural network (nnet)
can u explain me, how it works your code??? please. i´m also doing a simple
perceptron for homework on R and i dont know where to start.
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2007 May 06
3
Neural Nets (nnet) - evaluating success rate of predictions
Hello R-Users,
I have been using (nnet) by Ripley to train a neural net on a test dataset, I have obtained predictions for a validtion dataset using:
PP<-predict(nnetobject,validationdata)
Using PP I can find the -2 log likelihood for the validation datset.
However what I really want to know is how well my nueral net is doing at classifying my binary output variable. I am new to R and I
2009 May 24
2
accuracy of a neural net
Hi. I started with a file which was a sparse 982x923 matrix and where the
last column was a variable to be predicted. I did principle component
analysis on it and arrived at a new 982x923 matrix.
Then I ran the code below to get a neural network using nnet and then wanted
to get a confusion matrix or at least know how accurate the neural net was.
I used the first 22 principle components only for
2010 Dec 10
2
Help..Neural Network
Hi all,
I am trying to develop a neural network with single target variable and 5
input variables to predict the importance of input variables using R. I used
the packages nnet and RSNNS. But unfortunately I could not interpret the out
put properly and the documentation of that packages also not giving proper
direction. Please help me to find a good package with a proper documentation
for neural
2004 Aug 01
1
Neural Net Validation Sub Set
Dear R users,
I have been playing with the nnet and predict.nnet functions and have two questions.
1) Is it possible to specify a validation set as well as a training set in the nnet
function before using predict.nnet to test the nnet object against new data?
2) Is it possible to specify more than one layer of neurons?
Thanks in advance
Matt Oliver
2005 Feb 08
1
Toying with neural networks
Hello all,
Ive been playing with nnet (package 'nnet') and Ive come across this
problem. nnet doesnt seems to like to have more than 1000 weights. If I
do:
> data(iris)
> names(iris)[5] <- "species"
> net <- nnet(species ~ ., data=iris, size=124, maxit=10)
# weights: 995
initial value 309.342009
iter 10 value 21.668435
final value 21.668435
stopped after 10
2003 Jul 11
2
Nonliner Rgression using Neural Nnetworks
Hi,
I am an old hand at chemistry but a complete beginner at statistics
including R computations.
My question is whether you can carry out nonlinear
multivariate regression analysis in R using neural networks, where the
output variable can range from -Inf to + Inf., unlike discriminant
analysis where the output is confined to one or zero. The library nnet
seems to work only in the latter
2007 Feb 23
2
Neural Net forecasting
Are there any packages in R that are suitable for doing time series
forecasting using neural networks? I have looked in the nnet package and
neural package and they both seem geared towards classification.
thanks,
Spencer
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2000 Jul 22
1
maketitle garbles the title in package nnet (PR#613)
The TITLE for the nnet package is garbled: it comes out as
nnet Feed-forward neural networks and multinomial log-linear
nnet Feed-forward neural networks and multinomial log-linear models
The problem is in maketitle:
auk% cat DESCRIPTION
Bundle: VR
Version: 6.1-9
Date: 2000/07/11
Depends: R (>= 1.1)
Author: S original by Venables & Ripley.
R port by Brian Ripley
2010 Jan 03
2
Artificial Neural Networks
Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network?
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2006 Nov 22
1
What training algorithm does nnet package use?
Greetings list,
I've just swapped from the "neural" package to the "nnet" package and
I've noticed that the training is orders of magnitude faster, and the
results are way more accurate.
This leads me to wonder, what training algorithm is "nnet" using? Is
it a modification on the standard backpropagation? Or a completely
different algorithm? I'm
2004 Oct 18
1
nnet learning
Hi,
I am trying to make a neural network learning a "noisy sine wave".
Suppose I generate my data like so..
x <- seq(-2*pi, 2*pi, length=500)
y <- sin(x) + rnorm(500, sd=sqrt(0.075))
I then train the neural net on the first 400 points using
c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000,
abstol=0.075, decay=0.007)
Inspecting the fit of the training
2004 Jun 11
1
probabilistic neural networks
Hi,
I'm working on a classification problem and one of the methods I'd
like to use are neural networks. I've been using nnet to build a
classification network. However I would like to have the probabilities
associated with the prediction.
Are there any implementations of probabilistic neural networks available
in R?
thanks,
2003 Aug 20
1
Neural Networks in R
Hello!
We are a group of three students at Bielefeld University currently working
on a statistical projects about neural networks. Within the framework of this
project we are supposed to use the nnet-function in R and explain how it
works. Since anyone of us has much experience in using R we hoped to find some
information on your homepage. Unfortunatelly, we haven't been very successfull
so
2009 May 29
1
Backpropagation to adjust weights in a neural net when receiving new training examples
I want to create a neural network, and then everytime it receives new data,
instead of creating a new nnet, i want to use a backpropagation algorithm
to adjust the weights in the already created nn.
I'm using nnet package, I know that nn$wts gives the weights, but I cant
find out which weights belong to which conections so I could implement the
backpropagation algorithm myself.
But if anyone