similar to: convergence problem in boolean logit

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to

Hi there, I was trying to fit this dataset into LR model. This dataset includes 18 normal and 17 cancer. There are totally 14 markers (7 mRNAs and 7 Proteins). When I fitted into LR model, R gave me warning: Warning messages: 1: algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred

Hello, Listers I'm trying to run blloean logit model with R. My code is: > library(boolean) > library(foreign) > pr <- read.dta ("prcore1.dta") > bp <- boolprep ("(a&b)|c", "cwt", a="O1", b="t", c="DM2 > + ah + md + con + n3 + rel + slo + pyrs > + sp1 + sp2 + spl3") > answer <- boolean (bp, link =

Help needed with lm function: Dear R's, Could anyone tell me how to replace the link function (probit logit, loglog etc.) in lm with an abitrary user-defined function? The task is to perform ML Estimation of betas for a dichotome target variable. Maybe there is already a package for this (I did not find one). Any hints or a code excerpt would be welcome! Thank you -Jeff jeff.pr2 (at)

Run outfit<-nlm(..., hessian=T) and then standards error are se<-diag(solve(outfit$hessian))   Justin BEM BP 1917 Yaoundé Tél (237) 76043774   ________________________________ De : Bomee Park <bombom@stanford.edu> À : r-help@r-project.org Envoyé le : Jeudi, 29 Janvier 2009, 4h01mn 56s Objet : [R] standard error of logit parameters Hi everyone. I am now estimating the

Hello, I was trying a glm fitting (as shown below) and I got a warning and a fitted residual deviance larger than the null deviance. Is this the expected behavor of glm? I would expect that even though the warning might be warranted I should not get worse fitting with an additional covariate in the model. Could anyone tell me what I'm missing? I get the same results in both R2.5.1 on windows

Dear r helpers I run the following code for nested logit and got a message that Error in optim(c(0, 0, 0, 0, 0.1, -2, -0.2), fr, hessian = TRUE, method = "BFGS") : initial value in 'vmmin' is not finite What does this mean? and how can I correct it? Thank you June > yogurt = read.table("yogurtnp.csv", header=F,sep=",")> attach(yogurt)>

Hell all. I¡¯m trying to estimate mixed logit model using MSLE. In order to see that mixed logit model works better than simple logit model ( the logit model with fixed coefficient) I simulated a dataset with random coefficients and tried to fit the data with both mixed logit and simple logit model. Because my mixed logit model contains analytically intractable integrations, I applied

Hello All,  I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 =

Dear List, With regard to the question I previously raised, here is the result I obtained right now, brglm() does help, but there are two situations: 1) Classifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package:

I am sorry if this question sounds basic but I am having trouble understanding a warning message I have been receiving in R after attempting logistic regression. I have been using the logistic regression function in R to analyse a simulated data set. The dependent variable "failure" has an outcome of either 0 (success) or 1 (failure). Both the independent variables have been previously

Hey, everyone, I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE

Dear R-Helpers, Can anyone please help me to interpret warning messages from zeroinfl (package pscl) while fitting a zero inflated negative binomial model? The console reports convergence and the parameters seam reasonable, but these <<Warning messages: 1: algorithm did not converge in: glm.fit(X, Y, family = poisson()) 2: fitted rates numerically 0 occurred in: glm.fit(X, Y, family =

Dear all, can somebody please help me how to calculate "McFadden R-square" for a LOGIT model? Corresponding definition can be found here: http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_plum_statistics_rsq_mcfadden.htm Here is my data: Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,

Dear all, I am fitting a LOGIT model on this Data........... Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 47, 58, 82, 100, 222, 164, 161, 70, 219, 81, 209, 182, 185, 104, 126, 192, 95, 245, 97, 177, 125, 56, 85, 199, 298, 145, 78, 144, 178, 146, 132, 98, 120, 148, 123, 282, 79, 34, 104,

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Hello R-users. I haven't use R for a life time and this might be trivial - I hope you do not mind. I have a questions about arguments in the Glm-function. There seems to be something that I cannot cope. The basics are ok: > y <- as.double(rnorm(20) > .5) > logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit), trace = TRUE) Deviance = 28.34255 Iterations - 1

Dear friends, I'm doing a simulation on logistic regression model, but the programs can't work well,please help me to correct it and give some suggestions. My programs: data<-matrix(rnorm(400),ncol=8) #sample size is 50 data<-data.frame(data) names(data)<-c(paste("x",1:8,sep="")) #8 independent variables,x1-x8; #logistic regression model is

Hello, I have a data frame with 1425 observations, 539 of which are zeros. I am trying to fit the following ZINB: f3<-formula(Nbr_Abs~ Zone * Year + Source) ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData, offset=log(trans.area), trace=TRUE) Zone is a factor with 4 levels, Year a factor with 27 levels, and Source a factor with 3 levels. Nbr_Abs is counts

Hi all, I am using the clm function from the ordinal package to fit a cumulative logit model. If I run the regression without formatting my dependent variable (which is a factor), it works fine, however I need to change the order of the factors to be able to interpret the estimates. I have tried doing this using the relevel function and the factors are now in the correct order, but when running