search for: softmax

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 <- nnet(Yfac~ X1 +.. +X8,...

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 entropy=TRUE, linout=TRUE and cen...

...mize 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) p=softmax(z_g%*%b2); a=sum(y*log(p)+(1-y)*log(1-p)); return(a); } compute=function(b1) { a_g=NULL; for(i in 1:nrow(b1)){ a_g=cbind(a_g,X%*%b1[i,]) } return(a_g); } log.posterior=function(b1) { -log.like(b1)+1/2*t(as.vector(b1))%*%diag(as.vector(V))%*%as.vector(b1)...

...ode below): > imputed.england=mi(england.pre.imputed, n.iter=6, add.noise=FALSE) Beginning Multiple Imputation ( Mon Jan 14 13:39:49 2013 ): Iteration 1 Chain 1 : sex Error while imputing variable: sex , model: mi.categorical Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (3432) weights The error message indicates that there are too many weights (3432). I know you can adjust the maximum weights with the mi.categorical() function (i.e., MaxNWts= 3500), but I do not see a way to do this with the mi() function. I found an R-help posting from two...

...type="class")) setosa versicolor virginica setosa 50 0 0 versicolor 0 46 4 virginica 0 0 50 It works just fine, but if I do: > net <- nnet(species ~ ., data=iris, size=125, maxit=10) Error in nnet.default(x, y, w, softmax = TRUE, ...) : Too many (1003) weights Ive only changed 'size' from 124 to 125 giving me more than 1000 weights. Any ideas? Im I doing something wrong? > version _ platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 2 m...

...icas. 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 pial2 pial3a pial3b pial3cError en nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1068) weights -- buscando en foros encontre que debo modificar el nnet, concretamente maxNWts indicando un valor mayor al valor con problema, para modificar eso se me ocurre usar mice.impute.polyreg(dataset x=NULL, nnet.maxit = 100,nnet.trace = FALSE, nnet.maxNWts = 1500) p...

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]]

...y. Thanks for your help! Error message and system info below: Beginning Multiple Imputation ( Wed Jul 14 10:25:06 2010 ): Iteration 1 Imputation 1 : min.func* Error while imputing variable: min.func , model: mi.categorical Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (2608) weights System is Mac OS X 10.5.8, R version 2.9.2 Andrew Miles

...thanks for a hint Christoph ---------------------------------------------------------------------------- #--- neural networks #classification network is constructed; this has one output and entropy fit if the number of levels is two, and a number of outputs equal to the number of classes and a softmax output stage for more levels. -> therefore two lines of Prof. Ripley's wrapper function are changed below (original commented out) and an additional function has been introduced (resmatrix) con <- function(...) { print(tab <- table(...)) diag(tab) <- 0 cat("error r...

...rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling results (xyplot, stripplot, densitplot, histogram) - User-specified performance metrics for resampling - User-specified algorithms for determining the optimal tuning parameters (instead of highest/lowest) - A CHANGE...

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 number of weights to be less than what I need. Does anyone...

...rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling results (xyplot, stripplot, densitplot, histogram) - User-specified performance metrics for resampling - User-specified algorithms for determining the optimal tuning parameters (instead of highest/lowest) - A CHANGE...

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. Bes...

...icas. 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 pial2 pial3a pial3b pial3cError en nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1068) weights buscando en foros encontre que debo modificar el nnet, concretamente maxNWts indicando un valor mayor al valor con problema, para modificar eso se me ocurre usar mice.impute.polyreg(dataset x=NULL, nnet.maxit = 100,nnet.trace = FALSE, nnet.maxNWts = 1500) pero...

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

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

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