search for: maxits

Buenos dias R-help-es, Estoy interesado en estimar una curva dosis-respuesta para un conjunto de datos y para ello, estoy utilizando la libreria "drm". Hasta ahi todo bien. Me gustaria automatizar algunas cosas y el primer paso para ello es la estimacion del modelo. Si la estimacion funciona, todo lo demas funciona; de lo contrario, todo fallara. Tengo algunas lineas que mitigan un

Hi, I'm trying to extract a histogram over the results from a bootstrap. However I keep receiving the error message "Error in hist.default(boot.lrtest$ll, breaks = "scott") : 'x' must be numeric". The bootstrap I'm running looks like: > boot.test <- function(data, indeces, maxit=20) { + y1 <- fit1+e1[indeces] + mod1 <- glm(y1 ~ X1-1, maxit=maxit) +

Dear all, When I try to return some vectors from some functions within a function, it indicate an error," Error in rbind(ck1, ck2, ck3) : object 'ck1' not found", in one of the iterations and stop.  Since I am not experienced in programming, can anyone give me a suggestion to inspect this error? The followings are the functions I created : ################### # functions in the

Hello R-listers! My first post to the list is a very simple one for those who use the software continuosly. I am trying to understand the fixed-x resampling and random-x-resampling method proposed by Fox about Bootstrapping. The doubt that I have is on the side of the model run in one of the functions expressed for fixed-x resampling. What I don't understand is: X=model.matrix, and the -1

I typed the following commands but it still use maxiter=50 after the 2nd command: nls.control(maxiter = 1000) nls(......) Thanks! --------------------------------- [[alternative HTML version deleted]]

Hello, I'm using the nls function and would like to increase the number of iterations. According to the documentation as well as other postings on R-help, I've tried to do this using the "control" argument: nls(y ~ SSfpl(x, A, B, xmid, scal), data=my.data, control=nls.control(maxiter=200)) but no matter how much I increase "maxiter", I get the following error

Dear R users, I'm having a problem with maxiter specification in VGLM function. I tried to increase the number of iteration to 100, but it still stopped at 30, which is the default. Here is my script: FIT <- vglm(SFH_PCT ~ RD_DEN + CAR_HH + TRS + RES_L, tobit(Lower=0), maxiter = 100) Thanks Gary [[alternative HTML version deleted]]

Dear list, I was trying to use the R API for optimization method "Simulated annealing" void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex); but I encountered the following problem: The implementation of the function samin (as seen in src/main/optim.c) passes its void * argument "ex" into the function

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

I'm having problems (or to be precise a student is having problems, which I'm having problems helping her with) trying to use glm.nb() from the MASS package to do some negative binomial fits on a data set that is, admittedly, wildly overdispersed (some zeros and some numbers in the hundreds). glm.nb is failing to converge, and furthermore is (to my surprise) producing values of theta

Hi the list, I am writing a package in S4 and I do not manage to understand a bug. The "R CMD check" and the "R CMD build" both work. Here is links to the package (not on CRAN yet for the raison that I explain bellow): http://christophe.genolini.free.fr/aTelecharger/kml_0.5.zip http://christophe.genolini.free.fr/aTelecharger/kml_0.5.tar.gz Then I install the package and I

Dear R users, I am having some problems with glm. The first is an error message "subscript out of bounds". The second is the fact that reasonable starting values are not accepted by the function. To be more specific, here is an example: > success <- c(13,12,11,14,14,11,13,11,12) > failure <- c(0,0,0,0,0,0,0,2,2) > predictor <- c(0,80*5^(0:7)) >

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

Full_Name: Frederic Schutz Version: 1.6.1 OS: Linux Submission from: (NULL) (128.250.252.193) The subject says everything... This has already been proposed in June 2001 (cf http://maths.newcastle.edu.au/~rking/R/help/01a/2333.html), but no reply followed. I attach the patch that I used on my system to add this feature (including an updated help page), if it can help. Sorry, the two

Hello all. I have been working on a (fairly simple) function for way too long. I’ve really hit a wall and I was hoping someone might be able to point me in the right direction. I have (attempted) to create a function that has an embedded while loop. The while loop works fine by itself, however, when the while loop is embedded in the function, the function fails. I’m not sure why this

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Hi, I'm having trouble understanding how to construct a random number generator for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio statistic (under the null) for a linear model. The function at this point is given by boot.test.n01 <- function(data, indeces, maxit=20) { y1 <- fit1+se(e2)*rnorm(314) mod1 <- glm(y1 ~ X1-1, maxit=maxit) y2 <-

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Gracias! El 15 de junio de 2015, 16:54, Freddy Omar López Quintero < freddy.vate01 en gmail.com> escribió: > ?Holap.? > > ran out of iterations and failed to converge > > > ?Prueba aumentando el número de iteraciones, con el argumento maxit: > > ?GLM <- bigglm(In.hospital_death ~ GCS + BUN, data = DatosGLM, family = >> binomial(logit), maxit=1000)? >

I have a quick question, and I apologize in advance if, in asking, I expose my woeful ignorance of R and its packages. I am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example: