similar to: non-numerical values as input using nnet

Hi R-Helpers , am working on nnet package.Multinom() has an option for finding the goodness of fit by giving the AIC value. Does nnet also gives some value to determine the accuracy. If not, can you guide me with some procedure to figure out the accuracy/goodness of fit of nnet model? Thanks in advance. -- View this message in context:

Dear Sir/Madam I have recently started using the nnet package but cannot find any documentation other than the one page titled 'nnet {nnet}' which is replicated several times over the internet and is found in the help file for this package. I would like more information on how to use the package and have searched extensively over the internet but cannot find anything more. Do you know of

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 useRs: I am looking for a function which fits a multinomial model and in Baron?s page I find the function "multinom" in package "nnet" but this package is deprecated. I suppose that this function is now in other package but I can't find it. Can you help me? Thanks.

Hi, I am working on a project which needs a multinomial logit regression. So I want to reference the code of multinom in nnet package. I found nnet package is no longer in the CRAN list. But I cannot find the source code in R Core source code package either. Anyone knows how to see the source code of nnet? Luo

I am looking into using the multinomial logistic regression option in the nnet library and have two questions about formatting the data. 1. Can data be analysed in the following format or does it need to be transformed into count data, such as the housing data in MASS? Id Crime paranoia hallucinate toc disorg crimhist age 1 2 1 0 1 0 1 25 2 2 0 1 1 1 1 37 3 1 1 0 1 1 0 42 4 3 0

Hola todos Cuando realizo las predicciones del modelo multinomial con el paquete nnet, estas cambian cada vez que lo ejecuto ... saben por qué pasa esto ?? Gracias por la ayuda. [[alternative HTML version deleted]]

Dear All: I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not. I had posted earlier this example (thanks for the reply ronggui): worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),

Hi, Am working on neural network. Below is the coding and the output > library (nnet) > uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3) # weights: 16 initial value 4068.052704 final value 3434.194253 converged > summary (uplift.nn) a 3-3-1 network with 16 weights options were - b->h1 i1->h1 i2->h1 i3->h1 16.64 6.62 149.93

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 R people, I want to use the tune.nnet function of e1071 package to tune nnet . I am unable to understand the parameters of tune.nnet from the e1071 pdf document. I have performed nnet on a traindata and want to test it for class prediction with a testdata. I want to know the values of size,decay,range etc. parameters for which the prediction of testdata is best. Can anyone please tell me

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

Is AIC or BIC available when using the nnet package? Thank you Paul Green

Hello ! I'd like to know to which of the FANN package network corresponds the R nnet network ? In more details, what is the R nnet activation function, what is the training algorithm (rprop, quickprop, ...) ? Also, it seems that the R nnet "decay" parameter in nnet corresponds to the "learning_rate" parameter in FANN. Correct ? Many thanks in advance ! Luc Moulinier

Hi, I was trying to use the nnet library and am not sure of whats going on. I am calling the nnet function as: n <- nnet(x,y,size=3,subset=sets[[1]], maxit=200) Where x is a 272x4 matrix of observations (examples) and y is a 272x1 matrix of target values. However when I look at nnet$residuals they are off by two orders of magnitude (compared to the output from neural network code that I

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 R help, I run my data in nnet with skip layer, factor response (with 0 & 1 values) and explicitly put softmax=T to compare the result of the default nnet with no softmax specification. I assume this should give me the same result. I got the result the default one, but not the softmax version and I got the error message that I did not quite understand. test6.nn.skipT.softm.Yfac <-

Hi everyone, According to R reference manual, the nnet function uses the BFGS method of optim to optimize the neural network parameters. I would like, when calling the function nnet to tell the optim function not to produce the tracing information on the progress of the optimization, or at least to reduce the frequency of the reports. I tried the following: a) nnet default > x<-rnorm(20)

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

> 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