[R] Errors with maxNR: NA in gradient

[ruipbarradas at sapo.pt]

Hello,

Try using an equal sign after 'sigma':

algo=maxNR(loglikelihood,  
start=c(sigma=8.686603,beta1=-4.976215,beta2=7.313875))

Hope this helps,

Rui Barradas
 

Citando Emma MONTARSOLO <emsolo at hotmail.fr>:

> Hi !
>
> I use maxNR function (from maxLik package) to estimate parameters  
> via maximum likelihood and Newton–Raphson method.
>
> Context :
> I have a variable Y which follows a Gumbel distribution of  
> parameters Beta1X1 + Beta2X2 (location parameter) and sigma (scale  
> parameter).
> I have to estimate sigma, beta1 and beta2
>
> The following errors appear when I run maxNR :
>> algo=maxNR(loglikelihood,  
>> start=c(sigma:8.686603,beta1=-4.976215,beta2=7.313875))
>
> Iteration 1
> Parameter:
>                  beta1       beta2
>   4.251275 -249.985063    5.376905
>     beta1 beta2
> NaN   NaN   NaN
> Error in maxNRCompute(fn = function (theta, fnOrig, gradOrig = NULL,  
> hessOrig = NULL,  :
> NA in gradient
> In addition: There were 15 warnings (use warnings() to see them)
>> warnings()
>
> Messages d'avis :
> 1: In log(sigma) : NaNs produced
> 2: In log(sigma) : NaNs produced
> 3: In log(sigma) : NaNs produced
> 4: In log(sigma) : NaNs produced
> 5: In log(sigma) : NaNs produced
> 6: In log(sigma) : NaNs produced
> 7: In log(sigma) : NaNs produced
> 8: In log(sigma) : NaNs produced
> 9: In log(sigma) : NaNs produced
> 10: In log(sigma) : NaNs produced
> 11: In log(sigma) : NaNs produced
> 12: In log(sigma) : NaNs produced
> 13: In log(sigma) : NaNs produced
> 14: In log(sigma) : NaNs produced
> 15: In log(sigma) : NaNs produced
>
> My code is the following :
>
> n=length(data_reg$y)
>
> loglikelihood<-function(parameters)
> {
> sigma<-parameters[1]
> beta1<-parameters[2]
> beta2<-parameters[3]
>
> Z=(data_reg$y-(beta1*data_reg$x1)-(beta2*data_reg$x2))/sigma
> ll=-(n*log(sigma))+sum(Z+exp(-Z))
> ll
> }
>
> algo=maxNR(loglikelihood,  
> start=c(sigma:8.686603,beta1=-4.976215,beta2=7.313875))
>
> I use results from a previous estimation method as starting values.
>
> Thanks for your help. ��
>
> EM
>
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>
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