| fmle {FLCore} | R Documentation |
The fmle method fits the model specified in an FLModel object using MLE
by minimizing the negative of the log-likelihood function, in the logl slot,
through calls to the optim minimizaton routine.
For a given model and log-likelihood function, the fmle method will use the
optim function in R to calculate the parameter vector which maximises the log-
likelihood (and, hence, the likelihood function) and is as such the optimum parameter
value for the given problem and data.
Be advised that for non-informative of conflicting data the maximum likelihood estimate can be dependent on the initial starting value and if we begin the optimiser with a poor initial estimate it may converge falsely. Always try multiple start points and be assured that you ave found the true MLE.
fmle(object,start)
FLModel contains input data, logl function and function to provide initial values.FLModel contains input data and logl function, but fitting is started from parameter estimates in the FLPar object provided.The FLR Team
# use an example FLModel object data(nsher) summary(nsher) # inspect the logl function logl(nsher) # and the function providing initial values to the optimizer initial(nsher) # lower and upper limits for the parameters are set, and used if method # 'L-BFGS-B' is used in the call to optim, as is default in fmle lower(nsher) upper(nsher) # fit it with fmle nsher <- fmle(nsher) # fixed values can be chosen for any parameter nsher_fixed_a <- fmle(nsher, fixed=list(a=125)) # and results compared, for example using AIC AIC(nsher) AIC(nsher_fixed_a) ## Not run: # an initial run with one optimization method, e.g. 'SANN' nsher_one <- fmle(nsher, method='SANN') # can then be used as starting value for other runs # This might fail if nsher_two <- fmle(nsher_one, start=params(nsher_one), method='L-BFGS-B') ## End(Not run)