similar to: setting parameters to zero

Is anybody using the Copula package in R? The particular problem I'm facing is that R is not acknowledging the fitCopula command/function when I load the package and (try to) run something very simple: fit1 <- fitCopula(x1 = list(u11,u12,u13,u14,u15,u16,u17,u18), tCopula, optim.control = list(NULL), method = "BFGS") Anybody also using it, successfully or unsuccessfully?

Hello I'm trying to solve two sets of equations (each set has four equations and all of them share common parameters) with nlminb procedure. I minimize one set and use their parameters as initial values of other set, repeating this until their parameters become very close to each other. I have several parameters (say,param1, param2) and their constraints are given as inequality and depend

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

I use lmer to fit a mixed effect model.It give some warnings.what does this warnings mean? and what should I do? > (fm2.mlm <- lmer(qd ~ edu + jiankang + peixun +hunyin + cadcj + age + age2 + sex + dangyuan + Comp.1 + Comp.2+trust.cz1 +(trust.cz1|commid), data = individual,na.action = "na.exclude",family="quasibinomial")) Generalized linear mixed model fit using PQL

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

---------- Forwarded message ---------- Date: Fri, 4 Feb 2000 01:58:44 +0000 (GMT) From: Thomas Lumley <thomas@biostat.washington.edu> To: r-bugs@biostat.ku.dk Subject: constrained optimiser doesn't obey constraints The constrained optimiser is calling the function with parameter values outside the constrained range In this example the box is from c(0,0,0,0) to c(1,1,.5,1). The

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

Hallo together, I am trying to estimate parameters by means of QMLE using the nlminb optimizer for a tree-structured GARCH model. I face two problems. First, the optimizer returns error[8] false convergence if I estimate the functions below. I have estimated the model at first with nlm without any problems, but then I needed to add some constraints so i choose nlminb.

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hi All, Regarding cycle measurements for ARM/NEON, ARM no longer provide cycle accurate simulators. The method we use is to to make measurements on hardware via the PMU unit on the core itself. Note that if your running under Linux you may be 'allowed' to access the PMU directly but can access via it system calls. Typically you will need to make a series of measurements and average them.

#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 =

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

No error on Windows 10, R.3.5.2 patched, Rblas compiled with OpenBLAS 0.20, Rlapack is base. > f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) > opt <- nlminb(rep(0, 10), f, lower=-1, upper=3) > str(opt) List of 6 $ par : num [1:10] 1 1 1 1 1 ... $ objective : num -41.4 $ convergence: int 0 $ iterations : int 66 $ evaluations: Named int [1:2] 96 830 ..-

Good day, Here I was trying to write a code for Garch(1,1) . As garch problem is more or less an optimization problem I also tried to get the algorithm for "nlminb" function. What I saw that if use this function 'nlminb" I can easyly get the estimate of parameters. But any other function is not working. I tried to write my own code for optimization using Quasi-Newton

>>>>> Kasper Kristensen via R-devel >>>>> on Mon, 28 Jan 2019 08:56:39 +0000 writes: > I've noticed unstable behavior of nlminb on some Linux > systems. The problem can be reproduced by compiling > R-3.5.2 using gcc-8.2 and running the following snippet: > f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) > opt

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

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

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

Dear R-users I am trying to find a value for the risk free rate minimizing the difference between a BS call value with impl. volatilities minus the market price of a call (assuming this is just the average bid ask price) Here is my data: http://r.789695.n4.nabble.com/file/n3747509/S%26P_500_calls%2C_jan-jun_2010.csv S%26P_500_calls%2C_jan-jun_2010.csv S0 <- 1136.03 q <- 0.02145608 S0