similar to: Softmax in nnet

I am running the nnet package as > neural.soft<-nnet(custcat~region+ed+marital+tenure+age+address+income,size=3,softmax=TRUE) This returns the error message : formal argument "softmax" matched by multiple actual arguments Here the dependent variable "custcat" is a factor with 4-levels. This error does not crop up for any other arguments of nnet(), including

I am attempting to use optim to solve a neural network problem. I would like to optimize coefficients that are currently stored in a matrix Y=270 x 1 X= 27- x 14 b1= 10x14 b2= 11x1 V= 10 x 14 set of prior variances. I have the following function: posterior.mode1=function(y,X,b_0,b2,V) { log.like=function(b1) { a_g=compute(b1) z_g=tanh(a_g); z_g=cbind(1,z_g)

Dear Sirs. I have here running samba 2.2.4 compiled on Redhat 7.2 with kernel 2.4.17 with ACL support on EXT3 (http://acl.bestbits.at) and winbindd. Winbind works. I can ssh as a NT4 user and use samba network shares as a NT user without username mapping. (no German special characters, no spaces in user name). 1. In smb.conf veto files = /quota.*/lost+found/.*/ doesn't work 2. In

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

Dear all, I have discovered that there are many things that I used to do in my GIS which are easily done directly in R, for example calculating interpoint distances using geoR and pick out points inside a polygon using splancs. I now wonder, is there a function to create a line object like a watercourse and then calculate the distances between many points in space and this line? I couldn't

Saludo gente, antes que nada gracias por la ayuda que puedan aportarme, soy iniciante en R, estoy usando el paquete Mice para realizar imputaciones múltiples sobre variables en su mayoría categóricas. El problema está que cuando expresó este comando imp <- mice(dataset,method="polr",maxit=1) donde el dataset es un data.frame me tirá este error : iter imp variable 1 1 pial1a

Hi all, I am imputing missingness of  90 columns  in a  data frame using mice. But "mice" gives back :  Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE,  :   too many (1100) weights Any idea to solve this error is welcome, Anera [[alternative HTML version deleted]]

I am trying to use the mi package to impute data, but am running into problems with the functions it calls. For instance, I am trying to impute a categorical variable called "min.func." The mi() function calls the mi.categorical() function to deal with this variable, which in turn calls the nnet.default() function, and passes it a fixed parameter MaxNWts=1500. However, as

Hello, I am trying to impute data with the mi() function (mi package) and keep receiving an error message. When imputing the variable, "sex," the mi() function accesses the mi.categorical() function, which then accesses the nnet() function. I then receive the following error message (preceded by my code below): > imputed.england=mi(england.pre.imputed, n.iter=6, add.noise=FALSE)

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 I have some data (v. large amount) with a (0,1) response where I want to minimise the errors in the betas rather than SS or deviance. So can anyone point me to a ridge regression function or equivalent for such a logistic regression case? John -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

Hi, I'm trying to impute a large data set using mice but I keep getting this: Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (2944) weights nnet.default uses the argument MaxNWts to set a maximum number of weights. I've tried to change nnet.default to get around this, but mice is somehow still passing an argument that sets the maximum

Hello, Are there anyone knowing about the Softmaxreg R package? I try to understand the mathematics behind its models, by its command softmaxreg, especially the L2 regularized version. Is it regularized on the neural network layers or on the softmax regression layer? Are there any material/article or book on this? Thanks for any clarification. Bests, Vy O. [[alternative HTML version deleted]]

Saludo gente, antes que nada gracias por la ayuda que puedan aportarme, soy iniciante en R, estoy usando el paquete Mice para realizar imputaciones múltiples sobre variables en su mayoría categóricas. El problema está que cuando expresó este comando imp <- mice(dataset,method="polr",maxit=1) donde el dataset es un data.frame me tirá este error : iter imp variable 1 1 pial1a

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

Hello, I am VERY new to R (one week) and I am trying to run a multinomial logit model. The model I am using is > model1 <- multinom(Y ~ X1 + X2 + , ..., Xn) if I put in > summary(model1) I get #Error in function (classes, fdef, mtable) : unable to find an inherited method for function "fitted", for signature "multinom" and if I put in > coef(model1)

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

HI sorry to be a nuisance to all!!! how can i see the code of a particular function? e.g. nnet just as an example