search for: fnscale

Dear R-help, I'm using the 'optim' functions to minimise functions, and have read the documentation, but I'm still not sure how to determine sensible values to use for the 'fnscale' and 'parscale' options. If I have understood everything correctly, 'fnscale' should be used to scale the objective function, so that for example if the default is 'sensible' (or even 'optimal') for minimising 'f', one should use 'fnscale=1e-6' f...

Dear R users, I am trying to figure out the control parameter in "optim," especially, "fnscale" and "parscale." In the R docu., ------------------------------------------------------ fnscale An overall scaling to be applied to the value of fn and gr during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on fn(par)/fnsc...

...x[3]-400)/100)* ((x[4]-250)/50) + 0.62105*((x[3]-400)/100)* ((x[5]-550)/150) - 0.22426*((x[4]-250)/50)*((x[5]-550)/150))/219741978624 )} Then I run the following command, and I get a pretty good result (I know approximatively the theoritical maximum) optim(c(200,60,500,300,700),test,control=list(fnscale=-1))$par [1] 398.17520 31.34863 372.17182 217.30874 533.49458 Fortunately, all the optimal values fall in their range of possible value. But to be rigourous, I should perform a box-constrained optimization. Then, I do so with the following command (in fixing the lower and upper args and not):...

...vectors of data. I want to R to change the value of k to maximize the value of equation. My attempts at optim and optimize have been unsuccessful. Are these the recommended functions that I should use to maximize my equation? With optim I wanted the function to be maximized so I had to make the fnscale negative. Here is what I put: L<-optim(k,equation,control=(fnscale=-1)) My result: Error: could not find function "fn" Here is what I put for optimize: L<-optimise(equation,k,maximum=TRUE) My result: Error: 'xmin' not less than 'xmax' Any advise would be...

...sable from source() i.e., cannot be interpreted but must be built first. JN Example using <<- rm(list=ls()) optstart<-function(npar){ # create structure for optimization computations # npar is number of parameters ?? test?? OPCON<<-list(MAXIMIZE=TRUE, PARSCALE=rep(1,npar), FNSCALE=1, KFN=0, KGR=0, KHESS=0) # may be other stuff ls(OPCON) } add1<-function(){ OPCON$KFN<<-1+OPCON$KFN test<-OPCON$KFN } OPCON<<-list(MAXIMIZE=TRUE, PARSCALE=rep(1,4), FNSCALE=1, KFN=0, KGR=0, KHESS=0) ls(OPCON) print(add1()) print(add1()) print(ls....

...rep(0,nrow(RR)) theta.0 = numeric(3*K) for(i in 1:K) {theta.0[(1+3*(i-1)):(3*i)] = rep(1/10,3)+i/100} # data1 gives an error but data2 does not! constrOptim(theta=theta.0, f=likelihood, grad=score, ui=RR,ci=zeros, data = data1, method="BFGS", control=list(fnscale=-1,abstol=1e-16,reltol=1e-16))$par constrOptim(theta=theta.0, f=likelihood, grad=score, ui=RR,ci=zeros, data = data2, method="BFGS", control=list(fnscale=-1,abstol=1e-16,reltol=1e-16))$par # If you want to further check run the simulation below. K = 3 N...

Dear all, Firstly, I do apologize if my question is simple and posted in the wrong place but I had no reply from the R-help mailing list (maybe it is too simple!). I was wondering why parscale is set to 20 in the "wild" function example used in ?optim. This function has only one parameter and if we set parscale equal to 1 then the solution near the global minimum is not found. I

...optim(0.01, ## starting value binomial.ll, ## log likelihood method="BFGS", ## optimization method hessian=TRUE, ## numerial Hessian control=list(fnscale=-1), ## max, not min y=y, n=n) binomial.mle.par <- c(binomial.mle$par, -1/binomial.mle$hessian[1,1]) binomial.mle.par <- as.matrix(binomial.mle.par) rownames(binomial.mle.par) <- c("lambda", "s.e.") colnames(binomial.mle.par) <- c("MLE&quot...

...]*E^2 loglik <- sum(dgamma(data$lot1,shape=1/theta[3],rate=1/(E*theta[3]),log=T)) return(loglik) } # use the glm result as initial values theta <- c(as.vector(coef(fit1)),0.002446059) fit2 <- optim(theta, loglik, clotting, gr = NULL, hessian = TRUE, control = list(fnscale = -1)) # Then I got the following error message: Error in optim(theta, loglik, clotting, gr = NULL, hessian = TRUE, control = list(fnscale = -1)) : non-finite finite-difference value [3] Wayne (Yanwei) Zhang Statistical Research CNA Phone: 312-822-6296 Email: Yanwei.Zhang@cna.com<mailto:Yan...

...Y <- as.vector(y) xbeta <- X%*%par[1:K] Sig <- par[K1:K1] sum(-(1/2)*log(2*pi)-(1/2)*log(Sig^2)-(1/(2*Sig^2))*(Y-xbeta)^2) } llik.regress # Now let's use the above function to estimate the model. model <- optim(c(1,1,1,1), llik.regress, method="BFGS", control=list(fnscale=-1), hessian=TRUE) Error in optim(c(1, 1, 1, 1), llik.regress, method = "BFGS", control = list(fnscale = -1), : initial value in 'vmmin' is not finite -------------- next part -------------- # upload printing press data # 27 design points with 3 obs each for 81 tota...

?optim says, in describing the control parameter, 'fnscale' An overall scaling to be applied to the value of 'fn' and 'gr' during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on 'fn(par)/fnscale'. 'parscale' A vector of scaling val...

...vectors of data. I want to R to change the value of k to maximize the value of equation. My attempts at optim and optimize have been unsuccessful. Are these the recommended functions that I should use to maximize my equation? With optim I wanted the function to be maximized so I had to make the fnscale negative. Here is what I put: L<-optim(k,equation,control=(fnscale=-1)) My result: Error: could not find function "fn" Here is what I put for optimize: L<-optimise(equation,k,maximum=TRUE) My result: Error: 'xmin' not less than 'xmax' Any advise would be...

...optima and optimx to find the maxima, but it gives me value near by the highest mean as an output, for example 830 in the above parameters. The code for my optim function is val1=*optim*(par=c(X=1000, *seg=1*),fn=xnorm, opt=c(NA,seg=1), method="BFGS",lower=-Inf,upper=+Inf, control=list(fnscale=-1)) *I am running the optim function in a loop and for different initial value and taking the val1$par[1] as the best value.* *I am taking this parameter since I want to run it on n number of segments latter* and xnorm is simply calculating the dnorm *xnorm*=function(param, opt = rep(NA, 2)){...

...^(0:(n-1)))*log(x)) - 2000000*(sum(x)-k)^2 return(r) } gr <- function(x) { n <- length(x) r <- (b^(0:(n-1)))*(1/x) - 4000000*(sum(x)-k) return(r) } nvar <- 10 (sols <- optim(rep(20,nvar),f,gr,method="L-BFGS-B",lower=rep(0,nvar),upper=rep(k,nvar),control=list(fnscale=-1,parscale=rep(2000,nvar),factr=1e-300,pgtol=1e-300)))

...wrong? I am using R 2.5.0 on Fedora Core 6 (Linux). Thanks in advance, Paul ------------------------------------------------------ myfunc <- function(x) { x1 <- x[1] x2 <- x[2] abs(x1-x2) } optim(c(0.5,0.5),myfunc,lower=c(0,0),upper=c(1,1),method="L-BFGS-B",control=list(fnscale=-1))

...t <- (-1)*n*log(det(sigma)) logdet-sum(diag(A)) } optim(par=c(mu=mean(santes),delta=mean(sdespues-santes),s2ksi=422,s2eps=1,s2eta=75), fn=function(mu,delta,s2ksi,s2eps,s2eta) logvero(mu,delta,s2ksi,s2eps,0,s2eta,santes,sdespues-santes), control=list(trace=1, fnscale=-1), method="BFGS") gives the result: Error in as.vector(data) : Argument "s2ksi" is missing, with no default What is happening? Kjetil Halvorsen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://...

...ctor during each iteration. The documentation of optim() describes that the trace parameter should allow one to trace the progress of the optimization. I use the following command: optim(par = vPar, fn = calcLogLik, method = "BFGS", control = list(trace = TRUE, fnscale = -1, maxit = 2000)); which gives very little information: initial value 3.056998 final value 2.978351 converged Specifying trace >1, for instance trace = 20, does not result in more information. Is there a way to view more details of the progress perhaps by using another optimizer? Thanks...

...:q)] gr <- 2*Psi*((L^2)%*%e) gr } p <- ncol(cmat) if(is.null(start)) start <- (1 - 0.5*factors/p)/diag(solve(cmat)) res <- optim(start, FAfn, FAgr, method="L-BFGS-B", lower = lower, upper = 1, control = c(list(fnscale=1, parscale = rep(0.01, length(start))), control), q = factors, S = cmat) res } covmat <- structure(c(1, 0.0920030858518061, 0.053952442382614, -0.0380048634941013, 0.237986469993129, 0.243144461077282, 0.0920030858518061, 1, 0.328163804480881, 0.142002180914605, -0.13936961...

UseRs, I used the optim function valor.optim <- optim(c(1,1,1),logexp1,method ="BFGS",control=list(fnscale=-1),hessian=T); and I want to calculate the derivates, psi1<-valor.optim$par[1] psi2<-valor.optim$par[2] psi3<-valor.optim$par[3] a0=exp(psi1); a1=exp(psi2)/(20+exp(psi2)+exp(psi3)); a2=exp(psi3)/(20+exp(psi2)+exp(psi3)) deriv.psi1<-numericDeri...

> optim(c(1,1),LL,method="SANN",control=list(fnscale=-1),trans=trans,times=times) $par [1] 17.422635 -1.606859 How could i constraint that the parameters should be both positive in my maximizing problem? I check constrOptim but here i could only constraint the variables trans and times and not my parameters? many thanks, regards Christian