You haven't said how the function you're optimizing relates to your data.
In the special case that you happen to be using nleqslv to maximize a
log-likelihood function (a special case of which is least squares fitting),
you can get an approximation to the standard error using the Jacobian
matrix th
>
> Well, the delta method springs to mind, but it really depends on how and
> where noise is being injected into the system. All we have been told is that
> the estimates are obtained as a solution to a nonlinear equation, and that
> can mean many things. Presumably there are some observat
On Mar 20, 2012, at 15:55 , Berend Hasselman wrote:
>
> On 20-03-2012, at 15:36, FU-WEN LIANG wrote:
>
>> On Tue, Mar 20, 2012 at 4:24 AM, Berend Hasselman wrote:
>>>
>>>
>>> On 20-03-2012, at 01:01, FU-WEN LIANG wrote:
>>>
Dear R-users,
I use the "nleqslv" function to get p
On 20-03-2012, at 15:36, FU-WEN LIANG wrote:
> On Tue, Mar 20, 2012 at 4:24 AM, Berend Hasselman wrote:
>>
>>
>> On 20-03-2012, at 01:01, FU-WEN LIANG wrote:
>>
>>> Dear R-users,
>>>
>>> I use the "nleqslv" function to get parameter estimates by solving a
>>> system
>>> of non-linear equatio
On Tue, Mar 20, 2012 at 4:24 AM, Berend Hasselman wrote:
>
>
> On 20-03-2012, at 01:01, FU-WEN LIANG wrote:
>
> > Dear R-users,
> >
> > I use the "nleqslv" function to get parameter estimates by solving a
> > system
> > of non-linear equations. But I also need standard error for each of
> > estima
On 20-03-2012, at 01:01, FU-WEN LIANG wrote:
> Dear R-users,
>
> I use the "nleqslv" function to get parameter estimates by solving a system
> of non-linear equations. But I also need standard error for each of
> estimates. I checked the nleqslv manual but it didn't mention about SE.
> Is there
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