similar to: Question about glm using R

Hi I'm trying to fit a model in betareg and I'm getting errors, but have no idea what they mean or how to solve them. Does anyone have experience with this? > model <- betareg(ACT ~ ST*SoilT, data = actDL_F) Warning messages: 1: In sqrt(W) : NaNs produced 2: In sqrt(W) : NaNs produced 3: In sqrt(1 + phihat) : NaNs produced data summaries don't give any na's or problems I

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

I am having issues with the following: (muhat = 1/n^2(sum of all the xi's) ) essentially if xbar = the sample mean, muhat = sample mean but square the n. Question: Use R to run a Monte Carlo simulation which compares the finite-sample performance of xbar and muhat. Specifically generate 1000 samples n=30 from a standard normal distribution. For each sample calculate xbar and muhat. I have

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

Full_Name: Alex Deckmyn Version: 1.5.1 OS: Linux Mandrake 7.0 Submission from: (NULL) (193.190.63.62) A short function in a library contains the complex number -2i : phihat <- function(x) { x+ (-2i) } If i read the source file with source("phihat") this works fine. If I include it as a function in a library and read it with library(test), the function is still read

I have coded a time series from simulated data: simtimeseries <- arima.sim(n=1024,list(order=c(4,0,0),ar=c(2.7607, -3.8106, 2.6535, -0.9258),sd=sqrt(1))) #show roots are outside unit circle plot.ts(simtimeseries, xlab="", ylab="", main="Time Series of Simulated Data") # Yule ---------------------------------------------------------------------------- q1 <-

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Here's one possibility, assuming muhat and sigmahat are estimtes of mu and sigma from N iid draws of N(mu, sigma^2): tStat <- abs(x - muhat) / sigmahat pValue <- pt(tStat, df=N, lower=TRUE) I'm not quite sure what df tStat should have (exercise for math stat), but given fairly large N, that should make little difference. Andy > From: Ross Clement > > Hi. I have a

Dear R-helpers: We have had fun using betareg to fit models with proportions as dependent variables. However, in the analysis of these models we found some wrinkles and don't know where is the best place to start looking for a fix. The problems we see (so far) are that 1. predict ignores newdata 2. anova does not work Here is the small working example: ---------------------------- x

Hello, I have a dataset with proportions that vary around a fixed mean, is it possible to use betareg to look at variance in the dispersion parameter while keeping the mean fixed? I am very new to R but have tried the following: svec<-c(qlogis(mean(data1$scaled)),0,0,0) f<-betareg(scaled~-1 | expt_label + grouped_hpi, data=data1, link.phi="log",

Dear all, I get an error using betrag on this data set :http://dl.dropbox.com/u/1866110/dump.csv. I run it like this regression f2.1=betareg(Y~X1+X2,data=dump) summary(f2.1) I get : Call: betareg(formula = Y ~ X1 + X2, data = dump) Standardized weighted residuals 2: Error in quantile.default(x$residuals) : missing values and NaN's not allowed if 'na.rm' is FALSE In addition:

Folks, The betareg package appears to be polished and works well. But I would like to look at the exact formulas for the underlying model being estimated, the likelihood function, etc. E.g. if one has to compute \frac{\partial E(y)}{\partial x_i}, this requires careful calculations through these formulas. I read "Regression analysis of variates observed on (0,1): percentages, proportions and

Good morning, Does anyone know of a package or function to do a beta regression? Thanks, Walt Paczkowski _________________________________ Walter R. Paczkowski, Ph.D. Data Analytics Corp. 44 Hamilton Lane Plainsboro, NJ 08536 (V) 609-936-8999 (F) 609-936-3733

Dear all, we received a bug report for betareg, that in some cases the optim call in betareg.fit would hang the R session and the command cannot be interrupted by Ctrl-C? We narrowed down the problem to the dbeta function which is used for the log likelihood evaluation in betareg.fit. Particularly, the following command hangs the R session to a 100% CPU usage in all systems we tried it (OS X

Hi all user R, My simple question is...I have a vector of names of predictors, text<-c("datem","cola","eslom")...I try to plot the model with this predictor in sequence loop...., for(i in 1:3){ png(paste("fig_",i,sep="")) plot(preplot.gam(mod9)[[i]],se=T,rug=F,main="",xaxt="n",ylab="",xlab="")

Hi, what's mean "/" in command: betareg(inf~Grupo/Sexo, data=dados) it's a effect nested? -------------------------------------- Silvano Cesar da Costa Departamento de Estat?stica Universidade Estadual de Londrina Fone: 3371-4346

Hi, I want to fit 3 beta distributions to my data which ranges between 0 and 1. What are the functions that I can easily call and specify that 3 beta distributions should be fitted? I have already looked at normalmixEM and fitdistr but they dont seem to be applicable (normalmixEM is only for fitting normal dist and fitdistr will only fit 1 distribution, not 3). Is that right? Also, my data has 26

Hello, dear experts. I don't have much experience in building regression models, so sorry if this is too simple and not very interesting question. Currently I'm working on the model that have to predict proportion of the debt returned by the debtor in some period of time. So the dependent variable can be any number between 0 and 1 with very high probability of 0 (if there are no payment)

Hello, I want to determine the regression relationship between a proportion (y) and a continuous variable (x). Reading a number of sources (e.g. The R Book, Quick R,help), I believe I should be able to designate the model as: model<-glm(formula=proportion~x, family=binomial(link="logit")) this runs but gives me error messages: Warning message: In eval(expr, envir, enclos) :

Hi, In using the betareg package, I encounter the following error message: Error in lm.wfit(x, linkfun(y), weights, offset = offset) : NA/NaN/Inf in foreign function call (arg 4) Any help will be most appreciated. Thanks in advance. Alex