similar to: polr warning message optim

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

Hello splus-users, I am trying to fit a regression model for an ordered response factor. So I am using the function polr in library(MASS). My data is a matrix of 1665 rows and 63 columns (one of the column is the dependent variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap) but I am getting the following warning message singularity encountered in: nlminb.1(temp, p, liv, lv,

Dear Sir, I get an error message when I use polr() in MASS package. My data is "ord.dat". I made "y" a factor. y y1 y2 x lx 1 0 0 0 3.2e-02 -1.49485 2 0 0 0 3.2e-02 -1.49485 3 0 0 0 1.0e-01 -1.00000 4 0 0 0 1.0e-01 -1.00000 5 0 0 0 3.2e-01 -0.49485 6 0 0 0 3.2e-01 -0.49485 7 1 1 0 1.0e+00 0.00000 8 0 0 0 1.0e+00 0.00000 9 1 1 0

Hello again I am trying to fit an ordinal logistic model using the polr function from MASS. When I run model.loan.ordinal <- polr(loancat~age + sex + racgp + yrseduc + needlchg + gallery + sniffball + smokeball + sniffher + smokeher + nicocaine + inject + poly(year.of.int,3) + druginj + inj.years) I get an error Error in optim(start, fmin, gmin, method = "BFGS", hessian =

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

Hi, I have a problem with initial value for optim in polr that R report. After a call to polr, it complains that: Error in optim(start, fmin, gmin, method="BFGS", hessian= Hess, ...) : initial value in 'vmin' is not finite. Would you please suggest a way round to this problem? Thank you so much in advance. Rgds, - adschai

Hello from a presently sunny Helsinki! I've bee trying to repeat an analysis I did about 18 months ago (the reviewers of the paper want something adding to it). I'm using R1.5.0, but I couldn't see anything in the list of changes between this and 1.5.1 to suggest it would act any differently. The data is observational, on the changes in the population status of carabid beetles, and

Dear all, I get an error message when I use polr() in MASS. These are my data: skugg grupp frekv 4 1 gr3 0 5 2 gr3 3 6 3 gr3 6 10 1 gr5 1 11 2 gr5 12 12 3 gr5 1 > > summary(polr(skugg ~ grupp, weights=frekv, data= skugg.cpy1.dat)) Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) :

In polr.R the (several) functions gmin and fmin contain the code > theta <- beta[pc + 1L:q] > gamm <- c(-100, cumsum(c(theta[1L], exp(theta[-1L]))), 100) That's bad. There's no reason to suppose beta[pc+1L] is larger than -100 or that the cumulative sum is smaller than 100. For practical datasets those assumptions are frequently violated, causing the

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

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

I tried the following ordered logistic regression in R: mod1 <- polr(altitude~sp + wind_dir + wind_speed + hr, data=altioot) But when I asked The summary of my regression I got the folloing error message: > summary (mod1) Re-fitting to get Hessian Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : the initial value of 'vmin' is not

hello everybody I'm a newbie with ordered probit and with polr too. The problem is that I have a dependent variable I need to explain with an ordered probit that is > head(dfscale$sod.sit.ec.fam,100) > [1] 5 7 5 6 5 5 6 8 6 8 8 8 6 6 6 5 0 5 NA 6 > [21] 7 NA NA 0 0 2 5 3 6 6 7 6 NA 8 6 6 7 NA NA NA > [41] 4 5 NA 10 3 9 10 10 7 5 5 5 NA

Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt =

Sorry, I deleted my old post. Pasting the new query below. Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

Hello colleagues, This is a follow up to a question I posed in November regarding an analysis I was working on. Thank you to Dr. Brian Ripley and Dr. John Fox for helping me out during that time. I am conducting logistic regression on data set on psi (ESP) ganzfeld trials. The response variable is binary (correct/incorrect), with a 25% guessing base rate. Dr. Ripley suggested that I

Dear all, To modelize the abundance of fish (4 classes) with a set of environmental variables, I used the polr and predict.polr functions. I would like to know how to bring the cumulated probabilities back to a discrete ordinal scale. For the moment I used the predict.polr function with the argument "class". Is there an other way? polrf <- polrf <- polr_mod(formula =

Hello, I am running a model with four categories and want predicted probabilities in each category. Now for this example I wont give a counterfactual just the training data is fine but is there anyway to get a confidence interval around the predicted probabilities in each group? I have tried but it gives me probabilities and I have used interval="confidence", level=.095 and then interval