similar to: nnet plot

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

Dear all, the exclude and constant.weights options are used as follows: exclude: A matrix with n rows and 3 columns will exclude n weights. The the first column refers to the layer, the second column to the input neuron and the third column to the output neuron of the weight. constant.weights: A vector specifying the values of the weights that are excluded from the training process and treated

Hi, Our data has column names with spaces in that.The names in dataFrame are, *[1] "Sepal Length" "Sepal Width" "Petal Length" "Petal Width" "Species" * When i try to use the column names in rpart function, it gives the following error. * rp<-rpart(as.factor(`Species`)~`Sepal Length`) Error in `[.data.frame`(frame, predictors) :

Dear Frauke, Thank you very much for taking the time to respond. You explanation was very helpful, and I now have that part figured out! Best Wishes, Dan Frauke Message: 3 Date: Mon, 12 Oct 2020 08:33:44 +0200 (CEST) From: =?UTF-8?Q?Frauke_G=C3=BCnther?= <guenther at leibniz-bips.de> To: "r-help at r-project.org" <r-help at r-project.org> Cc: William Michels <wjm1

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

Hi Dr. Snow, & R-helpers, Thank you for your reply! I hadn't heard of the {microbenchmark} package & was excited to try it! Thank you for the suggestion! I did check the reference source for which() beforehand, which included the statement to remove NAa, and I didn't have any missing values or NAs: sum(is.na(dat$gender2)) sum(is.na(dat$gender)) sum(is.na(dat$y)) [1] 0 [1] 0 [1]

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

Hi It clear that to do a classification with svm under 10-fold cross validation one uses svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross = 10) What corresponds to the nnet? nnet(.....,cross=10)? Regards

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Good Afternoon, ?? I have a small problem with the following code. # The x.sub$Time[[1]] 2006-10-31 19:03:01 EST # when put in variable star give-me star<-x.sub$Time[[1]] print(star) print(x.sub$Time[[1]]) [1] 1 36 32 -........ do not understand why -- View this message in context: http://r.789695.n4.nabble.com/problem-with-data-tp4453510p4453510.html Sent from the R help mailing

Hello All, I am working with samr and nnet packages. I am following the steps given below: 1> I take a input file with signal values for 9506 genes and 36 chips , belonging to two classes. 2> I perform samr analysis on 80% of chip data from both the classes.(selected by random sampling) 3> I then use the data of only the significant genes from this samr analysis to train nnet. 4>

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

I am using library(nnet) to train up an ANN with what I believe is a moderately sized dataset, but R is complaining about too many weights: --- > nn.1 <- nnet(t(data), targets, size = 4, rang = 0.1, decay = 5e-4, maxit = 200) Error in nnet.default(t(data), targets, size = 4, rang = 0.1, decay = 5e-04, : Too many (1614) weights > dim(targets) [1] 146 2 > dim(data) ## Note

HI, Dear R community, My data set has 2409 variables, the last one is response variable. I have used the nnet after feature selection and works. But this time, I am using nnet to fit a model without feature selection. I got the following error information: > dim(train) [1] 1827 2409 nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=3, rang=0.3, decay=5e-4, maxit=500) # model

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

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

> nnet.fit<-nnet(as.factor(out) ~ ., data=all_h, size=5, rang=0.3, decay=5e-4, maxit=500) # model fitting > summary(nnet.fit) a 23-5-1 network with 126 weights options were - entropy fitting decay=5e-04 HI, Guys, I can not find the manual to describe how the model is built, is there a more detailed description how nnet package works? -- Sincerely, Changbin -- [[alternative