similar to: Function OPTIM()

Hello, I used optim to find the MLE estimates of some parameters. See the code below. It works for data1(x). but It did not work for data2 and the error says" L-BFGS-B needs finite values of 'fn' ". data2: c(x, 32) that is, if I added the number 32 at the end of data1. The error appears "non-finite function value" etc. Any comments or suggestions? Thanks!

Hi R users, I am trying to use the optim function to maximize a likelihood funciton, and I got the following warning messages. Could anyone explain to me what messege 31 means exactly? Is it a cause for concern? Since the value of convergence turns out to be zero, it means that the converging is successful, right? So can I assume that the parameter estimates generated thereafter are reliable MLE

Hi, there I am a graduate student new to coding in S who is hitting a bit of a wall at present using an "optim" function. I am running into some troubles, and was hoping someone might be able to recognize where I am going wrong. As background: I have constructed a loop that carries out a 365-day calculation for a mass-balance model. Basically, the model depends on 2 variables (p,

InDATA <-read.table("C:/Data/May 2004/season.txt",header=T) X <- decompose(InDATA) print(X) Period Connections Q1 67519 Q2 69713 Q3 68920 Q4 69452 Q1 70015 Q2 59273 Q3 57063 Q4 65596 Q1 73527 Q2 58586 Q3 69522 Q4 60091 Q1 51686 Q2 63490 Q3 55702 Q4 53200 Q1 51033 Q2 48175 Q3 52709 Q4 50106 Q1 50855 Q2 43466 Q3 48190 Q4 41702 Q1 48747 Q2 51441 Q3 42537

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

Dear R-helper, I am trying to do maximum likelihood estimation in R. I use the "optim" function. Since I have no prior information on the true values of the parameters, I just randomly select different sets of starting values to feed into the program. Each time, I get the following error message: Error in optim(theta0, lf, method = "BFGS", hessian = T, Y = Y, X = X, :

Hi R users, I am using the optim funciton to maximize a log likelihood function. My code is as follows: p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031, -0.00245, 3.366, 0.5885, -0.00008, 0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049, 0.1856, 0.00394, -0.00193, -0.889, 0.5379, -0.000063, 0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,

Having worked with optim() and related programs for years, it surprised me that I haven't noticed this before, but optim() is inconsistent in how it deals with bounds constraints specified at infinity. Here's an example: # optim-glitch-Ex.R x0<-c(1,2,3,4) fnt <- function(x, fscale=10){ yy <- length(x):1 val <- sum((yy*x)^2)*fscale } grt <- function(x, fscale=10){ nn

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

The the use of optim with the L-BFGS-B method for the following simple function gives erroneous results. Any help appreciated! Best, Bob Reilly # Code: V=function(p){ p1=p[1];p2=p[2] y=p1*p2-.4*(p1+p2) return(-y)} p=c(.2,.2) # p=c(.8,.8) max=optim(p,V,method = "L-BFGS-B",lower=c(0,0),upper=c(1,1)) max1=optim(max$par,V,method = "L-BFGS-B",lower=c(0,0),upper=c(1,1))

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

>>>>> Spencer Graves <spencer.graves at prodsyse.com> >>>>> on Sat, 8 Oct 2016 18:03:43 -0500 writes: [.............] > 2. It would be interesting to know if the > current algorithm behind optim and optimx with > method='L-BFGS-B' incorporates Morales and Nocedal (2011) > 'Remark on ?Algorithm 778:

Your results below make it look like a bug in optim(): it is not duplicating a value when it should, so changes to x affect xx as well. Duncan Murdoch On 03/05/2019 4:41 a.m., Serguei Sokol wrote: > On 03/05/2019 10:31, Serguei Sokol wrote: >> On 02/05/2019 21:35, Florian Gerber wrote: >>> Dear all, >>> >>> when using optim() for a function that uses the

Hi Spencer: See the link below about L-BFGS-B below because I had problems with it a good while back (and I think the link description is the cause but I can't prove it ) so eventually I moved to the Rvmmin(b) package. It's a package but really an algorithm. Rvmmin(b) uses a variable-metric algorithm similar to that of L-BFGS-B but without the problem below. It's not surprisingly a

On 02/05/2019 21:35, Florian Gerber wrote: > Dear all, > > when using optim() for a function that uses the parent environment, I > see the following unexpected behavior: > > makeFn <- function(){ > ??? xx <- ret <- NA > ??? fn <- function(x){ > ?????? if(!is.na(xx) && x==xx){ > ?????????? cat("x=", xx, ", ret=", ret, "

It looks as though this happens when calculating numerical gradients: x is reduced by eps, and fn is called; then x is increased by eps, and fn is called again. No check is made that x has other references after the first call to fn. I'll put together a patch if nobody else gets there first... Duncan Murdoch On 03/05/2019 7:13 a.m., peter dalgaard wrote: > Yes, I think you are

That's consistent/not surprising if the problem lies in the numerical gradient calculation step ... On 2019-05-06 10:06 a.m., Ravi Varadhan wrote: > Optim's Nelder-Mead works correctly for this example. > > >> optim(par=10, fn=fn, method="Nelder-Mead") > x=10, ret=100.02 (memory) > x=11, ret=121 (calculate) > x=9, ret=81 (calculate) > x=8, ret=64

Dear both, I have found that names are not treated in the same way in optim() depending on the optimization method (argument method). The example below shows the difference between the Brent method and the L-BFGS-B method. f <- function(x){ y <- x^2;names(y) <-"f(x)";y} optim(10, f, method="Brent", lower=-1, upper=10)$value optim(10, f, method="L-BFGS-B",

When optim() is used with method="BFGS", the name of parameters within the vector are transmitted (see below, first example). When method="Brent", the name of parameter (only one parameter can be fitted with Brent method) is not transmitted. As there is only one, of course, we know which parameter it is, but it makes things non-consistent between methods. It would be