similar to: Network reconfiguration glitch after migration

# Hi All, # # I need to solve a somewhat complex equation at many parameter values for # a number of different parameters. # A simplified version of the equation is: 0= (d1/(h1^2))-(h2*(d2^2)) # I'd like to solve it across a parameter space of d1 and d2, holding # h1 and h2 constant. # It seems that uniroot() can do it, but I don't see how to vectorize it. #

Hi R-users, I am trying to estimate function parameters using optim(). My count observations follows a Poisson like distribution. The problem is that I wanna express the lambda coefficient, in the passion likelihood function, as a linear function of other covariates (and thus of other coefficients). The codes that I am using (except data frame) are the following (FYI the parameters need to be

On Mon, Mar 26, 2018 at 15:57:01 +0300, Apollon Oikonomopoulos wrote: ... > I'd be happy to test the patch, thanks! Ok, try the attached patch. (It is a first pass at the issue, so it may not be the final diff that'll end up getting committed. It'd be good to know if it actually fixes the issue for you - sadly, I don't have a big endian system to play with.) Thanks, Jeff.

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

I am trying to multiply a 3D array of 4x4x4 by the 4 3D arrays of a 4D array with dimensions 4x4x4x4 (the last dimension being the one that I want to split by). (4x4x4 array) > hiaAry , , a1 i1 i2 i3 i4 h1 9.5936098 6.001040 0.08772 0.3138600 h2 1.2003500 1.454570 2.79248 0.0000000 h3 0.1346500 0.201220 0.39256 0.5464000 h4 0.0109000 0.012270 0.16417 0.2766900 ,

Hello, I'm trying to calculate a certain distance estimator for my thesis. I have a program in C that works fine when I call it with .C() in R, but since I'm dealing with big matrices like 30000x20000 it was getting a stack overflow. Now I have the same program but more efficeintly coded using malloc, and it works perfectlry in C, compiles well with R CMD SHLIB but when I call it with

Hi all, Here's my story. I have 3 hosts, 1 remote and 2 local. We'll say for the sake of clarity that h1 is remote and h2 is the local rsync pull host and h3 is also a local host on the net. Using rsync, I want to pull a data set first from h1 to both h2 and h3. The first pull copies all the data from h1 to h2 and h3. OK, that gives me a baseline of the data on both h2 and h3.

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

Dear all, I've been struggling for days now with a piece of code that I have posted here before, that has a really obscure bug. I think I may have isolated it, but I have no idea what it is.... It might also be a bug in R I guess, as it seems that one or several of list elements are not passed when a function is called, but quite rarely. I have been hacking rather wildly on the histogram

Dear all! I have a few questions. I'm running Version 1.0.0 on Digital UNIX. My problem is as follows: I have hundreds of megabytes in images stored in a few hundred files, and I want to extract some basic characteristics about the images in these files. For a start, I need to find the density function of all the files combined. Since it's several hundred MBs, I haven't enough RAM to

Hi, I'm having trouble within my function CalcPos to get it to call CalcHorz with values from each row. I *think* it's calling CalcHorz with the final values of the inputs and not the values from each row. How can I do this properly in R? The values aa,bb,cc,dd are inputs. CalcPos first calculates V1 and V2 vertically, and then I attempt to call CalcHorz to handle H1, H2 & H3

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Full_Name: Suharto Anggono Version: 2.8.1 OS: Windows Submission from: (NULL) (125.165.81.124) Sorry for repost. There is already PR#9602, but the problem is still there. There is also a post "Re: [R] using bquote to construct function" in R-help 2008-10-02. This illustrates the problem. C:\Program Files\R\R-2.8.1\bin>R --vanilla R version 2.8.1 (2008-12-22) Copyright (C) 2008

Hello to the R people, within ward clustering the distance calculated to decide the clustering of 2 subsets (h1 and h2) is the variation of inertia : d(h1,h2)=I(h1Uh2)-I(h1)-I(h2); i've been said that a way to calculate faster this d(h1,h2) is using the huygens theorem decomposing the inertia into "the inertia to the centroid + the distance to an axe" (that's my version ...). My

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

On 25/01/11 00:17, David A. Greene wrote: [...] > The rewrite is happening. I've got the skeleton of the codegen done, > but I have to get it to build before I can check it in. After that, > everyone can start adding patterns. Is the new C backend 'register' based, that is, generating lots of little statements operating on lots of variables, rather than producing the huge

Hi R-devel. Is the following behavior in g1() and h1() expected? It seems to make "..." arguments work slightly differently from named arguments. #missing() has the property that it looks "up the chain" #for example, "z" can be missing in f3 even if #that argument did have a name ("y") in f2 f1 <- function(x, ...) { cat("In f1, missing(x) is

Hi, I have two KVM host: h1 and h2, both of them mount an NFS directory as a shared storage. I can save (virsh save <domain> <file>) a domain in h1 to a state file in the shared storage successfully, but failed to restore it from h2 with the following error message: # virsh restore testRes.dat error: Failed to restore domain from testRes.dat error: operation failed: failed to start VM

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Many thanks for the answers on my previous question, it got me started. Indeed, stack() was the function I was vaguely remembering. However, I didn?t get very far because my data set is way more complicated then I expected. In fact I have a mixture of levels and lists within a list. Basically, it resemble the following list (named data) made of the levels H and the list of lists A and T. for each