messages from mle function

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.

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)
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.

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)
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