similar to: nlminb to optmin

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

This is a follow-up question for PR#15052 <http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15052> There is another thing I would like to discuss wrt how nlminb() should proceed with NAs. The question is: What would be a successful way to deal with an evaluation point of the objective function where the gradient and the hessian are not well defined? If the gradient and the hessian both

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'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

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

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

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

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 am having the same problem as one Rebecca Sela(see bellow). On 21/12/2007 12:07 AM, Rebecca Sela wrote: >* I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo

Hi all ! I've been trying to maximize a likelihood using optim( ) function, but it seems that the function has several local maxima. I've tried in my algorithm with different starting values and depending on them "optim" obtains different results... I use the "L-BFGS-B" method setting the lower values as 1e-06, because my parameters must be strictly positive. Also

#optim package estimate<-optim(init.par,Linn,hessian=TRUE, method=c("L-BFGS-B"),control = list(trace=1,abstol=0.001),lower=c(0,0,0,0,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf),upper=c(1,1,1,1,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf)) #nlminb package estimate<-nlminb(init.par,Linn,gr=NULL,hessian=TRUE,control =

Dear R developers, I tried to use nlminb instead of optim for a current problem (fitting parameters of a differential equation model). The PORT algorithm converged much better than any of optim's methods and the identified parameters are plausible. However, it took me a while before spotting the reason of a technical problem that nlminb, in contrast to optim, does not pass names of the

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

What's the current best approach to dealing with the nlminb problem? Are there other similar constrained optimizers, or is current wisdom just to code it in C (or similar...)? best, -tony -- A.J. Rossini Research Assistant Professor of Biostatistics Biostatistics/Univ. of Washington (Th) Box 357232 206-543-1044 (3286=fax) Center for AIDS Research/HMC/UW (M/F) Box 359931

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)>

Hi all, I've been working on improving R's optim() command, which does general purpose unconstrained optimization. Obviously, this is important for many statistics computations, such as maximum likelihood, method of moments, etc. I have focused my efforts of the BFGS method, mainly because it best matches my current projects. Here's a quick summary of what I've done: *

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error: > lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr) Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, : initial value in 'vmmin' is not