similar to: difference between nlm and nlminb

Hello all, I am trying to get the variance-covariance (VCOV) matrix of the parameter estimates produced from the nlminb minimizing function, using vcov.nlminb, but it seems to have been expunged from the MASS library. The hessian from nlminb is also producing NaNs, although the estimates seems to be right, so I can't VCOV that way either. I also tried using the vcov function after minimizing

Hola a todos, Necesito estimar dos parametros utilizando la función nlm; fit<-nlm(hood2par,c(x01[i],x02[j]),iterlim=300, catch=x[,c(3,4,5)],sp=.5) donde hood2par es una logística modificada. Pero en mi caso, la convergencia de nlm depende de los valores iniciales de dichos parámetros. Para buscar dichos valores iniciales de manera automática, genero dos vectores de valores iniciales

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Hi All, I am looking into merging 3 data sets I know how to do that by merging data1 with data2 and then merging the result with data 3. I was wondering if it can be done all at once so I tried, M<-merge(data1,data2,data3, by=?ID?) It does not work! Any ideas? -- View this message in context: http://www.nabble.com/merging-3-data-sets-at-once-tp17658873p17658873.html Sent from the R help

Running GPL PV Windows drivers on Windows 2003 Server Standard Edition SP2 - The current GPL PV Windows xennet driver source found in the repository will BSOD if the option "Large Send Offload" is changed to disabled or if the driver is disabled. Tracked down the issue via WinDbg – If a buffer is in the free list prior to the function XenNet_RxBufferFree() being called via the function

It would be really nice if nlm took a set of "..." optional arguments that were passed through to the objective function. This level of hacking is probably slightly beyond me: is there a reason it would be technically difficult/inefficient? (I have a vague memory that it used to work this way either in S-PLUS or in some previous version of R, but I could easily be wrong.) Here's

I have a 2x2 plot set up using: par(mfrow=c(2,2)) I'd like to put an overall title on the page, but I cannot figure out how. Any ideas? [[alternative HTML version deleted]]

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

Dear list, Here's a suggestion about the different optimization code. There are several optimization procedures in the base package (optim, optimize, nlm, nlminb, ..). However, the output of these functions are slightly different. For instance, 1. optim returns a list with arguments par (the estimates), value the minimum (maxima) of the objective function, convergence (optim

Hello I want to use the nlminb function but I have the objective function like characters. I can summarize the problem using the first example in the nlminb documentation. x <- rnbinom(100, mu = 10, size = 10) hdev <- function(par) -sum(dnbinom(x, mu = par[1], size = par[2], log = TRUE)) nlminb(c(9, 12), objective=hdev) With the last instructions we obtain appropriate results. If I have

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

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

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

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 datasets, and a few different (but very similar) likelihood functions. (They are complicated, but I can send them

I won't add to the quite long discussion about the vagaries of nlminb, but will note that over a long period of software work in this optimization area I've found a number of programs and packages that do strange things when the objective is a function of a single parameter. Some methods quite explicitly throw an error when n<2. It seems nlminb does not, but that does not mean that

#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 =

Hi! I want to convert S-Plus 6.2 code to R 2.1.0. Instead of the function nlminb I use the function optmin optmin(start,fn,gr,method="L-BFGS-B", lower, upper, hess,...) But then I get the Error in optmin ...: L-BFGS-B needs finite values of fn Then I used optmin(start,fn,gr,method="BFGS", hess, ...) But then I get the Error in optmin ...: initial value in vmmin is not

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

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