[R] Robust SE & Heteroskedasticity-consistent estimation

[RATIARISON Eric]

Thank you all of you,;
I submit you my case:
( Pseudo likehood for exponential family with offset )


loglik <- function(param)  {
   b<-param
   m=as.vector(of+z%*%b)
   ll <- sum(v*m-exp(m))   }


gradlik <- function(param) {
     b<-param
     m=as.vector(of+z%*%b)
     gg<-(v-exp(m))*z      }

hesslik <- function(param) {
     b<-param
     m=as.vector(of+z%*%b)
     hh <- -t(exp(m)*z)%*%z }

resMaxlik <- maxLik(loglik,grad=gradlik,hess=hesslik,start=sc,method="nr",tol=1e-4);

n<-NROW(v)
k<-NROW(resMaxlik$estimate)

meat <-function(obj,nrow,npar,adjust=FALSE,...) 
{ psi<-obj$gradient
  k<-npar
  n<-nrow
  rval<-crossprod(as.matrix(psi))/n
  if (adjust) rval <- n/(n-k)*rval
  rval
}

M<-as.vector(meat(resMaxlik,n,k))
B <-as.matrix(ginv(resMaxlik$hessian))

vcovhc=B*M*B

#new standard errors
coeftest(resMaxlik,vcov.=vcovhc)

 
do you think it's sound good ?




-----Message d'origine-----
De : Arne Henningsen [mailto:arne.henningsen at googlemail.com] 
Envoyé : mardi 11 mai 2010 08:25
À : Achim Zeileis; RATIARISON Eric; r-help at r-project.org; Ott-Siim Toomet
Objet : Re: [R] Robust SE & Heteroskedasticity-consistent estimation

On 11 May 2010 00:52, Achim Zeileis <Achim.Zeileis at uibk.ac.at> wrote:
> On Mon, 10 May 2010, RATIARISON Eric wrote:
>> I'm using maxlik with functions specified (L, his gradient & hessian).
>>
>> Now I would like determine some robust standard errors of my estimators.
>>
>> So I 'm try to use vcovHC, or hccm or robcov for example
>>
>> but in use one of them with my result of maxlik, I've a the following
>> error message :
>>
>> Erreur dans terms.default(object) : no terms component
>>
>> Is there some attributes to give to maxlik objet for "fitting" the call
>> of vcovHC?
>
> This is discussed in
>  vignette("sandwich-OOP", package = "sandwich")
> one of the vignettes accompanying the "sandwich" package that provides the
> vcovHC() function. At the very least, you need an estfun() method which
> extracts the gradient contributions per observation. Then you need a bread()
> function, typically based on the observed Hessian. Then you can compute the
> basic sandwich() estimators.

Is it possible to implement the estfun() method and the bread()
function in the maxLik package so that vcovHC() can be easily used by
all users of the maxLik package? If yes: should we (Eric, Achim, Ott,
Arne) implement this feature together?

/Arne

-- 
Arne Henningsen
http://www.arne-henningsen.name