similar to: NNET re-building the model

Hi All, I am trying to use a neural network for my work, but I am not sure about my approach to select a parsimonious model. In R with nnet, the IAC has not been defined for a feed-forward neural network with a single hidden layer. Is this because it does not make sens mathematically in this case? For example, is this pseudo code sensible? Thanks in advance for your help. I am sorry if this

Hi all, I'm having some difficulty passing arguments into lm() from within a function, and I was hoping someone wiser in the ways of R could tell me what I'm doing wrong. I have the following: lmwrap <- function(...) { wts <- somefunction() print(wts) # This works, wts has the values I expect fit <- lm(weights=wts,...) return(fit) } If I call my function lmwrap, I get

Hi there, I´ve a question concerning the nnet package in the area of survival analysis: what is the final value, which is computed to fit the model with the following nnet-c all: net <- nnet(cat~x, data=d, size=2, decay=0.1, censored=TRUE, maxit=20, Wts=rep(0,22), Hess=TRUE) where cat is a matrix with a row for each record and

Hi I got two questions about the nnet function in R. I would be thankful to have an answer. 1) Does the function intrinsically normalize the X and Y matrices before the training, or normalization should be done by the user. 2) I need to understand the $wts matrix. I do imagine that it is a single column transformation of the two matrices of weighs (input to hidden Ninputs+1 x Nodes) and

Hi, I am doing double for loops to calculate SDs with some weights and wondering if I can get rid of the outer for loop as well. I made a simple examples which is essentially what I am doing. Thanks for your help! -Young #------------------------------------------------------ # wtd.var is Hmisc package # you can replace the 3 lines inside for loop as # sdx[i,] =

Hi, I am trying to determine the number of units in the hidden layer and the decay rate using the CVnn2 script found in MASS directory (reference: pg 348,MASS-4). The model that I am using is in the form of Y ~ X1 + X2 + X3... + X11 and the underlying data is time-series in nature. I found the MASS and nnet package extremely useful (many thanks to the contributors). However I am getting

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone

Hi everyone, I admit I am a bit of an R novice, and I was hoping someone could help me with this error message: Warning message: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments weigths are just disregarded. My equation is: lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1)) -- View this message in context:

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

All: I am new to R and would like your help in identifying the appropriate process to follow in order to modify the output from an existing package. I've had difficulty finding an answer online, perhaps because I am using incorrect terminology. A package that I am using (mmlcr) invokes another package (multinom). An output of multinom is the standard errors, but this output is not

Dear All, I'm preparing a simple algorithm for matrix multiplication for a specific purpose, but I'm getting some unexpected results. If anyone could give a clue, I would really appreciate. Basically what I want to do is a simple matrix multiplication: (WB) %*% t(WB). The WB is in the disk so I compared to approaches: - Load 'WB' using 'read.table' (put it in WB.tmp) and

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Hi. I am running SAMBA 2.0.7 on HP-UX 11.00 and HP-UX 10.20. I have tried adding the MultipleUsersOnConnection registry key onto my Microsoft Windows NT Server 4 Terminal Server Edition servers, running with Service Pack 6. After I make this change in the registry and reboot my WTS servers all my samba connections from the servers are still being made with one process instead of being split

The following warning message occurs when running the nls on some data: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Warning message: In is.na(wts) : is.na() applied to non-(list or vector) of type 'NULL' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ and I would like to know what is causing it and how I can fix it. As an example, the following, from Venables and Ripley, which

My code is to minimize the objective function therefore, shouldnt I expect that StartingValue = c(0.12, 0.04, 0.07, 0.03, 0.06, 0.07, 0.07, 0.04, 0.09, 0.08, 0.02, 0.02, 0.03, 0.06, 0.02, 0, 0.07, 0.05, 0.02, 0.02, 0.02) Fn(q1$par) < Fn(StartingValue) ## FALSE Below is the corrected code that can be reproduced: MyDat = structure(list(c(50L, 0L, 0L, 50L, 75L, 100L, 50L, 0L, 50L, 0L, 25L,

?s 13:59 de 28/03/2025, Daniel Lobo escreveu: > Hi Duncan, > > Thanks for your comment, I agree with that. > > But, how it can be justified that an Optimizer gives a result which is > inferior to the starting value? At most, resulting value can remain at the > same level, isnt it? > > On Fri, 28 Mar 2025 at 14:34, Duncan Murdoch <murdoch.duncan at gmail.com>

?s 19:36 de 27/03/2025, Daniel Lobo escreveu: > My code is to minimize the objective function > > therefore, shouldnt I expect that > > StartingValue = c(0.12, 0.04, 0.07, 0.03, 0.06, 0.07, 0.07, 0.04, 0.09, > 0.08, 0.02, 0.02, 0.03, 0.06, 0.02, 0, 0.07, 0.05, 0.02, 0.02, 0.02) > Fn(q1$par) < Fn(StartingValue) > ## FALSE > > Below is the corrected code that can

?s 18:35 de 27/03/2025, Daniel Lobo escreveu: > Hi, > > I have below minimization problem > > > MyDat = structure(list(c(50L, 0L, 0L, 50L, 75L, 100L, 50L, 0L, 50L, 0L, > 25L, 50L, 50L, 75L, 75L, 75L, 0L, 75L, 75L, 75L, 0L, 25L, 75L, > 75L, 0L, 75L, 100L, 0L, 25L, 100L), c(75L, 0L, 0L, 50L, 100L, > 50L, 75L, 75L, 100L, 25L, 0L, 25L, 100L, 0L, 50L, 0L, 25L, 25L, >

I need help on calling the svydesign function in the survey package (although this error appears not to be specific to svydesign). I am passing parameters incorrectly but am not sure how to correct the problem. ## Call the main function PS.sim (one of mine). The dots are parameters I omitted to simplify the question. ## y.col, str.col, clus.id, and PS.col are names of columns in the object pop.