I know my convergence problem is not a new one to this email list.  I looked
at old post and I still can't figure out my problem.  This is why I am
sending this email.  

 

Here is my situation:  

 

I have back-calculated fish growth using otoliths (ear bones).  In a
nutshell you can back calculate the length of the fish at each age by
measuring the distance from the core of the otolith to each annuli and the
edge of the otolith.  Since the data is longitudinal, autocorrelated, and
unbalanced the best way to generate a growth curve is using a non-linear
mixed effect model (Vigliola and Meekan 2009).  

 

I have three columns of data: fish identification number (id), length at age
(L), and incremental age (Age).  

 

Here is my code:  

#first I create my growth model which is the von Bertalanffy growth model
(LVB)

>LVB=function(x,t0,Lmax,K){                                               

>y=Lmax*(1-exp(-K*(x-t0)))


>y

>}

 

#I define the groups for the mixed effect model.  

>datagr=groupedData(L~Age|id,data=back)

 

#The next step fits a von Bertalanffy growth model by non-linear mixed
effect model.  

>LVB.nlme=nlme(L~LVB(Age,t0,Lmax,K), data=datagr, 

>fixed=list(t0~1,Lmax~1,K~1),         #The model is estimating global values
for all three parameters. 

>random=t0+Lmax+K~1,                  #The model is estimating values for
each parameter for 

each individual fish.  This is the random effect part of the model.  

>start=list(fixed=c(t0=-1, Lmax=700, K=0.2)))         #list creates a list
of the arguments, fixed means 1 single fixed effect, c means create a data
vector.  

 

I keep getting the error of "Maximum number of iterations".  I have tried
different starting values and still have no luck.  I even tried 

increasing the number of iterations to 1,000 using nlmeControl.  

 

Any advice on how to get my model to converge would be greatly appreciated.


 

Mike 


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