R help - Mar 2013 - initial starting values with constOptim()

Hi all,

I am trying to estimate few parameters using the constained maximum
likelihood in R and more specifically the constOptim() from the stata
package in R. I am programming in Python and using R via the RPy2.

In my model, I am assuming that the data follow the Beta-distribution, so I
created a simulated dataset by using prespecified values for the parameters
and now I am trying to estimate these parameters in order to verify that my
estimation program works fine.

What I have observed is that my estimation is quite sensitive to the
initial parameters. For example I have 11 parameters to estimate (let's
call the parameters as pam1..pam11) and their true value is:

pam1=0.2 pam2=0.3 pam3=0.4 pam4=0.7 pam5=0.55 pam6=0.45 pam7=0.1 pam8=0.01
pam9=0.01 pam10=45 pam11=45

In the contrtOptim() I am setting the starting parameters as:

start_param=FloatVector((pam1,pam2,pam3,pam4,pam5,pam6,pam7,pam8,pam9,pam10,pam,11))

where I set the starting values. I have observed that when I am using
different sets of starting values the results change. For example when I am
using the set

start_param=FloatVector((0.2,0.3,0.4,0.6,0.7,0.8,0.3,0.011,0.011,15,15))

and I obtain the following estimates

$par

 [1]  0.20851065  0.30348571  0.43616932  0.73695654  0.58287221 0.45541506

 [7]  0.11191879  0.02233908  0.01988878 46.57249043 45.48544918

$value
[1] -215.9711

$convergence
[1] 0

but when I am using another set as for example:

start_param=FloatVector((0.2,0.3,0.4,0.75,0.55,0.45,0.3,0.05,0.05,59,59))

the results change and it seems that I am loosing convergence

$par
[1]  0.17218738  0.27165359  0.48458978  0.80295773  0.62618983  0.43254786
[7]  0.12426385  0.02991442  0.01853252 57.78269692 59.35376216

$value
[1] -146.9858

$convergence
[1] 1

My question is the following: I have seen that in STATA, there is an option
that searches for better starting values for the numerical optimization
algorithm. I tried to set multiple starting values by setting a matrix but
this did not work. Is there an option in constrOptim that will allow me to
do something like this?

Many thanks in advance.

For additional information, the specification I use for the constrOptim()
 is:

res=statsr.constrOptim(start_param,Rmaxlikelihood,grad='NULL',ui=ui,ci=ci,method="Nelder-Mead",control=list("maxit=3000,trace=F"))

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