search for: maxnwt

...ble: 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 years ago with some instruction on how to change the max weights in mi(), but I can't seem to get it to work. The following is a link to the post: http://r.789695.n4.nabble.com/Changing-model-...

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 know how to change the parameters that mice is passing to nnet/nnet.default?...

...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 you can see below, the min.func variable needs a higher threshold than 1500. Is there a way that I can change the MaxNWts parameter that is being sent to nnet.default()? I've investigated the mi() and mi.info() functions and cannot see a way. Thanks for your help!...

...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 el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categórica...

...he two files I am attaching with this message. http://r.789695.n4.nabble.com/file/n2240582/3dMaskDump.txt 3dMaskDump.txt http://r.789695.n4.nabble.com/file/n2240582/test_vowels.txt test_vowels.txt Question: when I am attempting to run >cvc_nnet <- nnet(G ~ ., data=cvc_lda, size=1,iter=10,MaxNWts=100) I get an error saying: Error in as.data.frame.default(x[[i]], optional = TRUE) : cannot coerce class c("terms", "formula") into a data.frame I have not encountered this error when I was running this script with previous lda results, and, I am not quite sure what the er...

...that I am using is this: library (MASS) heap_lda <- data.frame(as.matrix(t(read.table(file="R_10_5runs_matrix9.txt")))*100000,syll = c(rep("heap",3),rep("hoop",3),rep("hop",3))) library(nnet) heap_nnet <- nnet(syll ~ ., data=heap_lda, size=12,iter=100,MaxNWts=10000) predict(heap_nnet,heap_lda,type = "class") table(predict(heap_nnet,heap_lda,type = "class"),heap_lda$syll) # do leave-one-out crossvalidation... heap_nnet.out<-NULL all = c(1:9) for(n in all){ heap_nnet <- nnet(syll ~ ., data=heap_lda[all != n,], CV =TRUE,siz...

...data, model and parameters are below: choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182); sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129); case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2); result <- multinom(case ~ choles + sbp + choles:sbp, abstol=1.0e-20, reltol=1.0e-20, MaxNWts=10000); However, the estimated coeffcients and standard errors of the coefficeints are different from the SPSS. For instance, the estimated coefficients of the variable "choles" are 0.1946555 and 0.6244513 from the above result, but the SPSS result are 0.213120 and 0.662575. Standar...

...de 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 el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categórica...

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

The following code fails to train a nnet model in a random dataset using caret: nR <- 700 nCol <- 2000 myCtrl <- trainControl(method="cv", number=3, preProcOptions=NULL, classProbs = TRUE, summaryFunction = twoClassSummary) trX <- data.frame(replicate(nR, rnorm(nCol))) trY <- runif(1)*trX[,1]*trX[,2]^2+runif(1)*trX[,3]/trX[,4] trY <-

...+ netbuffor + strmslope + netsoilprm + netslope + gwndx + mnaspect + urb + ag + forest + buffor + tempa7day + tempb7day + tempc7day + tempd7day + tempe7day + tempa30day + tempb30day + tempc30day + tempd30day + tempe30day, data = temp.train, method = "nnet", linout=T, maxit = 100, MaxNWts = 100000, metric = "RMSE", trControl = fitControl, tuneGrid = tuning, trace = T)

...s are selected? For example, if I weighted each of my positive examples with weight 5 and my negative examples with 1, would the positive examples then be selected and shown to the network as frequently as the negative examples (since there are 200 compared with 1000)? This is my aim. 2. The MaxNWts parameter seems to suggest that the number of nodes/weights changes as the network is trained. Is this the case? I haven't supplied any examples since my questions are of a more theoretical nature. Thank you in advance for your comments, Hannah [[alternative HTML version deleted]]

...by random sampling) 3> I then use the data of only the significant genes from this samr analysis to train nnet. 4> The parameters I am currently using for nnet are: result <- nnet(traindata[sortsamp,], targets[sortsamp,], size = nnetsize , rang =0.00000003 ,decay = 0.00009, maxit = 100, MaxNWts =100000) traindata is the significant gene's data, sortsamp is the randomly sampled number out of those genes and targets is the class indicator of the significant genes. 5> Then I am using the data from the chips left out from samr(20% chip data) to test the nnet. I us...

Dear R people, I am using tune.nnet from e1071 package to tune the parameters for nnet. I am using the following syntax: tuneclass <-c(rep(1,46),rep(2,15)) tunennet <-tune.nnet(x=traindata,y=tuneclass,size=c(50,75,100),decay=c(0,0.005,0.010),MaxNWts = 20000) Here traindata is the training data that I want to tune for nnet which is a matrix with 61 rows(samples) and 200 columns(genes). tuneclass is the class indicator vector which says that the first 46 samples of the traindata are of class 1 and the next 15 samples are of class 2 type....

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

...de 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 el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categórica...

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

...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 el problema que me piden >> X que representa Matrix (n x p) of complete covariates, pero...

...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 el problema que me piden > X que representa Matrix (n x p) of complete covariates, pero al ser las &...