messages from mle function
This is not reproducible, so I am reduced to guessing. Your loglike() is only defined for positive f, but you have not imposed that constraint on your optimization. Further, if f=5.91e-05 is a typical value, your problem is badly conditioned and the finite difference code in optim() is likely to step outside the undeclared region of validity. I suggest that you - study the help page for optim() - make use of the control parameters to scale the problem suitably - either impose the positivity constraint via the L-BFGS-B method, or transform f to a suitable scale, e.g. by making log(f) the parameter.
On Tue, 11 Mar 2008, bernardo lagos alvarez wrote:
Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me.
You could look at the many warnings -- they may well tell you the underlying problem.
thank you for you atention. Bernardo.
erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE)
head(erizo)
EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5
TAN <- function(edad,f,c,a,d) (1/sqrt(f))*log(abs(2*f*(edad-c)+ 2*sqrt((f^2)*((edad-c)^2)+f*a)))+d
loglike = function(f,c,a,d) {
+ edad <- erizo$EDAD + LT <- erizo$TALLA + N <- length(edad) + sigma <- sum((LT - TAN(edad,f,c,a,d))^2) / N + logl <- (N/2)*log(sigma) + (sum((LT - TAN(edad,f,c,a,d))^2) / (2*sigma)) + }
ini.pars <- list(f=5.91e-05,c=-0.41732,a=0.009661,d=846.7179) library(stats4) erizo.mle <- mle(start= ini.pars, minuslogl = loglike, method="Nelder-Mead", control = list(maxit=1500, trace=TRUE))
Nelder-Mead direct search function minimizer
function value for initial parameters = 1159.477620
Scaled convergence tolerance is 1.72776e-05
Stepsize computed as 84.671790
BUILD 5 3165.307359 1159.477620
.
.
.
HI-REDUCTION 303 1158.377359 1158.377314
LO-REDUCTION 305 1158.377339 1158.377303
LO-REDUCTION 307 1158.377321 1158.377303
Exiting from Nelder Mead minimizer
309 function evaluations used
Error en optim(start, f, method = method, hessian = TRUE, ...) :
non-finite finite-difference value [1]
Adem?s: Hubo 50 o m?s avisos (use warnings() para ver los primeros 50)
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Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595