similar to: initial value in 'vmmin' is not finite

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems

My decision tree grows only with one split and based on what I see in E-Miner it should split on more variables. How can I adjust splitting criteria in R? Also is there way to indicate that some variables are binary, like variable Info_G is binary so in the results would be nice to see "2) Info_G=0" instead of "2) Info_G<0.5". Thank you in advance! And thanks for Eric who

Greetings, I have a nagging problem with contrasts and I can't seem to resolve it. A factor exists with four levels (lib1, lib2, con1, con2) and when I check the contrasts or set the contrasts to any of the prespecified ones, I do not get the exact contrasts necessary to test the theoretically relevant ones. I need orthogonal contrasts that look just like this matrix: con1 con2

Hi, folks, Please first look at the codes: plan_a=c('apple','orange','apple','apple','pear','bread') plan_b=c('bread','bread','orange','bread','bread','yogurt') value=1:6 data=data.frame(plan_a,plan_b,value) library(plyr) library(reshape) mm=melt(data, id=c('plan_a','plan_b'))

-------- Original-Nachricht -------- Betreff: Re: [R] Adding and Multiplying two Unevaluated Expressions Datum: Tue, 01 Dec 2009 23:49:39 +0100 Von: Benjamin M?ller <ben_mueller.bm at web.de> An: Rolf Turner <r.turner at auckland.ac.nz> Referenzen: <20091201144125.316310 at gmx.net> <8E40E49F-E8FC-4FBD-8CC5-93789FFB0E53 at auckland.ac.nz> This works fine for your

Hello all - I am sorry if this simple question is addressed in somewhere else. But, I couldn't find it. It's been for about a week using R. . . My problem is: Reading matrix data with "scan" does not seem to allow me to incorporate matrix labels (columns, and rows). If I use read.table, I can import the lables. But, the problem is, I don't know how I make this data as

Many thanks to the R core team for their work on R connections. I was delighted to discover that it is now possible to remote-control R-1.3.0 via a socket connection - something which Brian Ripley noted was not yet possible with R-1.2.0 back in January 2001 in the first R News newsletter. How? Just substitute source() for readLines() in last few lines of the example for a socket connection given

The `gzcon` function both modifies and copies a connection object: # compressed text con1 <- url("http://www.stats.ox.ac.uk/pub/datasets/csb/ch12.dat.gz") con2 <- gzcon(con1) # almost indistinguishable con1==con2 identical(summary(con2), summary(con1)) # both support gzip readLines(con1, n = 3) readLines(con2, n = 3) # opening one opens both isOpen(con2)

I am calling 'vmmin' several times from a C function (which is called via .C). It works very well, except for memory consumption. The cause is that vmmin allocates memory via R_alloc, and this memory is not freed as vmmin exits. Instead all the allocated memory is freed on return of the .C call. In one application, I have 2000 functions of 500 variables each to minimize. In each call to

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Error in optim(method = "BFGS", c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : initial value in 'vmmin' is not finite I am running a logit model with latent class segments. I successfully got estimates for 2 segments. However when I tried to increase the no. of segments, I got this error message at the end. I checked my code again but can't find anything wrong. Is this error

Dear r helpers, I'm trying to run the models of the BIOMOD package and got this message: * Error in nnet.default (x, y, w,...): **initial value in 'vmmin' is not finite* What does this mean? and how can I correct it? Thank you *Andreia* [[alternative HTML version deleted]]

I am writing code in C and would like to call R's functions samin and vmmin (optimization routines: simulated annealing and BFGS) I do not understand how to create and pass in the function (as well as the extra arguments it needs) I am optimizing. I have read the R Extensions manual but it is still unclear to me. Could you give me some pointers and/or direct me to some example code which

Dear r-helpers, I'm experiencing some problems in fitting a maximum likelihood binomial model to some of my data. The error is in optim, which founds: Error in optim(par = c(0.2, 0.5), fn = function (p) : initial value in 'vmmin' is not finite Yes, I know it's a common problem, and I've carefully searched and readed all the posts about the issue, But, I can't find a

Dear listers, Am currently using MCMC approaches to estimate some parameters of my model. One parameter has to be updated using a tuned gamma distribution. So at each iteration I estimate the mean and variance of the density of the gamma approximation using "vmmin" (i also supply the gradient argument). For moderate replications the procedure works, but if I increase them R crashes.

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Hi all. I'm just starting to explore ordinal multinomial regression. My dataset is 300,000 rows, with an outcome (ordinal factor from 1 to 9) and five independent variables (all continuous). My first stab at it was this: pomod <- polr(Npf ~ o_stddev + o_skewness + o_kurtosis + o_acl_1e + dispersal, rlc, Hess=TRUE) And that worked; I got a good model fit. However, a variety of other

This is what I get on R 3.3.1 on Linux: > con1 <- textConnection("foo1", open = "w") > print(con1) description class mode text "foo1" "textConnection" "w" "text" opened can read can write "opened"

Hi all, I used `gzfile` and `gzcon` to read a compressed file but I found that `gzcon` gave me a different result than `gzfile`. It seems like the `gzcon` does not handle the data correctly. I have posted an example below. In the example, a portion of a compressed file is downloaded from Google Cloud as a raw vector, and the data is saved into a temp file. If I use ` gzfile` to read the file, it