On 5/10/2013 5:22 AM, Ott Toomet wrote:
Hi,
maybe we changed the convergence criteria a little bit? I cannot remember,
but we have played around with those a few times. You may try those,
perhaps it helps a bit.
Another recommendation---as you do not supply analytic gradient, you may
want use BHHH method instead (if this applies to your problem), or another
one that does not need Hessian. Numeric approximation of Hessian is pretty
error-prone..
You can try all the different "method" options for "maxLik", pick
the best answer, iterate once to make sure you can't get something
better. If you want a hessian and don't get one from the best answer,
you can feed the best answer to the "hessian" function to get that.
Also, if you get a singular hessian, that says that you cannot
estimate as many parameters as you have. If you have p parameters and
the hessian is of rank p0 < p, that says you can only estimate p0
parameters independently. You can use the "fnSubset" function to fix
(p-p0) parameters at specific values and see what you get from varying
the others.
Estimating hessians can be error-prone whether you use numeric or
analytic derivatives. The "compareDerivatives" function can help you
check analytic derivatives for software bugs.
Hope this helps.
Spencer
Best,
Ott
On Fri, May 10, 2013 at 2:40 PM, Arne Henningsen
<arne.henning...@gmail.com>wrote:
Dear Alfonso
On 10 May 2013 12:51, <alfonso.carf...@uniparthenope.it> wrote:
we are computing maximum likelihood estimations using maxLik package and
we
realized that the results of the estimation depend on the version of the
package installed
for example if we try to estimate this function with an old version of
maxLik under R 2.13.1 (32 bit version installed 2 years ago):
L<-function (param) {b0t<-param[1]
p1t<-param[2]
p2t<-param[3]
p3t<-param[4]
p4t<-param[5]
for(i in 17:T) {n[i,]<- b0t + p1t*a[i-1] + p2t*sum(a[(i-4):(i-1)]) +
p3t*(sum(a[(i-8):(i-1)])) + p4t*(sum(a[(i-16):(i-1)]))
m[i,]<-exp(n[i])/(1+exp(n[i]))
ll[i-16,]<-a[i]*log(m[i])+(1-a[i])*log(1-m[i]) }
sum(ll)}
b2<-maxLik(L, start=c(-2.8158,5,-1,0.3213,-0.3112))
we obtain this results:
summary(b2)
Maximum Likelihood estimation
Newton-Raphson maximisation, 16 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -38.11285
5 free parameters
Estimates:
Estimate Std. error t value Pr(> t)
[1,] -2.81578 0.43548 -6.4660 1.007e-10 ***
[2,] 50.50024 13.17046 3.8344 0.0001259 ***
[3,] -11.53344 3.31075 -3.4836 0.0004947 ***
[4,] 0.32130 0.42978 0.7476 0.4547096
[5,] -0.31121 0.23245 -1.3388 0.1806280
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
--------------------------------------------
NB: a is a binary time series
if we try to estimate the same function using the last version of maxLik
under R.3.0 (64 bit latest version) the estimation do not converge and
this
is the error message:
Iteration 15
Parameter:
[1] -2.8146429 51.3042554 -11.7373313 0.3245214 -0.3125767
Gradient:
[1] NaN NaN NaN NaN NaN
Errore in maxNRCompute(fn = logLikAttr, fnOrig = fn, gradOrig = grad,
hessOrig = hess, :
NA in gradient
What causes this?
It could be that the NaNs in the gradients are caused by rounding
errors and/or approximation errors in the numerical
(finite-difference) derivatives when using R.3.0 64 bit, because
different hardware and different software versions (e.g. R,
mathematical libraries, OS) could lead to different rounding errors.
In this case, the specification of a function that returns analytical
gradients could solve the problem.
If this does not solve the problem and you cannot find out the reason
for the NaNs in the analytical gradients yourself, please provide a
reproducible example so that we could help you with this.
Please note that you could also ask questions regarding the maxLik
package via a forum at maxLik's R-Forge site:
https://r-forge.r-project.org/projects/maxlik/
... and please do not forget to cite maxLik in your publications :-)
Best regards,
Arne
--
Arne Henningsen
http://www.arne-henningsen.name
--
Spencer Graves, PE, PhD
President and Chief Technology Officer
Structure Inspection and Monitoring, Inc.
751 Emerson Ct.
San José, CA 95126
ph: 408-655-4567
web: www.structuremonitoring.com
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