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...obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res = mle(minuslog=Model,start=list(alpha12=0.1,w_u12=0.1,w_g12=0.1), method = "BFGS") res Error message: > res = mle(minuslog=Model,start=list(alpha12=0.1, w_u12=0.1, w_g12=0.1), method = "BFGS") Error in optim(start, f, method = method, hessian = TRUE, ...) : non-finite finite-diffe...

...xample to find MLE for gamma distribution from "fitting distributions with R": library(stats4) ## loading package stats4 ll<-function(lambda,alfa) {n<-200 x<-x.gam -n*alfa*log(lambda)+n*log(gamma(alfa))-(alfa- 1)*sum(log(x))+lambda*sum(x)} ## -log-likelihood function est<-mle(minuslog=ll, start=list(lambda=2,alfa=1)) Is anyone how how to write down -log-likelihood function for noncentral t distribution? Thanks a lot!! Kate [[alternative HTML version deleted]]

...function(lambda_log, alfa_log) { + n <- length(interarrival_times) + x <- interarrival_times + -n*exp(alfa_log)*lambda_log + n*log(gamma(exp(alfa_log))) - (exp(alfa_log)-1)*sum(log(x)) + exp(lambda_log)*sum(x) + } and passed it to the mle function: > est_gamma <- mle(minuslog = nll_gamma_log, start = list(lambda_log = 0, alfa_log=0)) There were 50 or more warnings (use warnings() to see the first 50) of course I got many "value out of range" warnings: > warnings() Warning messages: 1: In log(gamma(exp(log_alfa))) ... : value out of range in 'gammafn...

...i am sure, there is a clear way how to do it, no? #shouldn't i actually get more or less the same parameterestimates with both #methods? library(stats4) > ll<-function(alfa,beta) + {n<-24 + x<-data2 + -n*log(alfa)-n*log(beta)+alfa*sum(x^beta)-(beta-1)*sum(log(x))} > est<-mle(minuslog=ll, start=list(alfa=10, beta=1)) There were 50 or more warnings (use warnings() to see the first 50) > summary(est) Maximum likelihood estimation Call: mle(minuslogl = ll, start = list(alfa = 10, beta = 1)) Coefficients: Estimate Std. Error alfa 0.002530163 0.0006828505 beta 0.641873...

...39;\n' or ';' or '}' in "ll<-function(lambda,alfa){n<-200;x<-x.gam -n*alfa*log(lambda)+n*log(gamma(alfa))-9alfa" > ll<-function(lambda,alfa){n<-200;x<-x.gam -n*alfa*log(lambda)+n*log(gamma(alfa))-(alfa-1)*sum(log(x))+lambda*sum(x)} > est<-mle(minuslog=ll,start=list(lambda=2,alfa=1)) Error in optim(start, f, method = method, hessian = TRUE, ...) : objective function in optim evaluates to length 200 not 1 audaces fortuna iuvat --------------------------------- [[alternative HTML version deleted]]

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(...

...ates?How do you choose the starting values? in my opinion it should be around 1/mean(data). #Parameterestimation ??with mle() with the log-likelihood funktion of the ?? #exponentialdistribution library(stats4) ll<-function(beta) {n<-24 x<-data2 -n*log(beta)+beta*sum(x)} est<-mle(minuslog=ll, start=list(beta=0.1)) summary(est) #instead of a result, i get: Error in optim(start, f, method = method, hessian = TRUE, ...) : ?? ?? ?? ?? non-finite finite-difference value [1] In addition: There were 50 or more warnings (use warnings() to see the first 50) #with fitdistr() for the expo...

...it some data I have (called ONES20) to the non-central chi-squared distribution. >library(stats4) >ll<-function(lambda,k){x<-ONES20; 25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*k-1,expon.scaled=FALSE))} > est<-mle(minuslog=ll,start=list(lambda=0.05,k=0.006)) R accepts the function definition without a problem, but gives this error when I ask for the mle of the parameters. Error in besselI(x, nu, 1 + as.logical(expon.scaled)) : Non-numeric argument to mathematical function In addition: Warning messa...

...{dgamma(y, shape=a, rate=b)} integ_res <- tryCatch({integrate(integrand,min_x,max_x)$value}, error=function(err){0}); if (integ_res == 0) { return(NA) } C = 1 / integ_res res = -(sum(log(C*dgamma(x,shape=a,rate=b)))) return(res) } m <- mean(x) v <- var(x) fit <- mle(minuslog=ll_gamma,start=list(a=m^2/v,b=m/v)) Now, for some reason I get very weird results. I have tested it by sampling random numbers from gamma distribution, for example, and then try to fit it with the algorithm I wrote. Am I doing something wrong? do I need to first fit the sample with regular gamma...

...ting the code below, I just obtain the result for t=1. ################ library(stats4) likelihood <- function(v1,v2,v3){ for (t in 1:136){ return(-(sum(E1_f[t,]*(v1*teta1[] + v2*teta2[] + v3*teta3[]) - E_f[t,]*log(1 + exp(v1*teta1[] + v2*teta2[] + v3*teta3[]))))) } } L_f <- mle(minuslog=likelihood, start=list(v1=1, v2=2, v3=3)) x <- summary(L_f) ################ What should I change in the code? And how can I store the values of v1(t), v2(t) and v3(t) in 3 vectors, in order to use them after? Thank you very much. Roger

...; > >>> ll<-function(lambda,alfa) {n<-200 > > >>> x<-x.gam > > >>> -n*alfa*log(lambda)+n*log(gamma(alfa))-(alfa- > > >>> 1)*sum(log(x))+lambda*sum(x)} ## -log-likelihood function > > >>> est<-mle(minuslog=ll, start=list(lambda=2,alfa=1)) > > >>> > > >>> Is anyone how how to write down -log-likelihood function for > noncentral t > > >>> distribution? > > >> > > >> Just use dt. E.g. > > >> >...