On Tue, 30 Oct 2007, Michael Ash wrote:
> On 10/30/07, Christian Ritz <[EMAIL PROTECTED]> wrote:
> > in the MLE setting the score function (no expectation taken) is the
> > estimating function. So for the OLS situation the basic estimating
> > function is: (in the terminology of Zeileis' paper)
>
On 10/30/07, Christian Ritz <[EMAIL PROTECTED]> wrote:
> in the MLE setting the score function (no expectation taken) is the
> estimating function. So for the OLS situation the basic estimating
> function is: (in the terminology of Zeileis' paper)
>
> psi(x,y,beta) = (y - x^t beta) x^t
Thanks! Th
Hi again,
in the MLE setting the score function (no expectation taken) is the
estimating function. So for the OLS situation the basic estimating
function is: (in the terminology of Zeileis' paper)
psi(x,y,beta) = (y - x^t beta) x^t
Christian
__
R
On 10/30/07, Christian Ritz <[EMAIL PROTECTED]> wrote:
> Use 'model.matrix' to construct a design matrix:
>
> x <- runif(10,0,10)
> model.matrix(~x)
Thanks! That's super helpful.
> The following article by A. Zeileis provides more details on the
> package 'sandwich':
> http://www.jstatsoft.org/
Dear Michael,
as to questions 1) and 2):
The following article by A. Zeileis provides more details on the
package 'sandwich':
http://www.jstatsoft.org/v16/i09/
(see also the references).
Use 'model.matrix' to construct a design matrix:
x <- runif(10,0,10)
model.matrix(~x)
Christian
Dear R-help,
I have a four-part question about regression, matrices, and sandwich package.
1) In the sandwich package, I would like to better understand the
meat() function.
>From the bread() documentation, for a simple OLS regression, bread() returns
(1/n * X'X)^(-1)
That is, for a simple reg
Dear R-help,
I have a four-part question about regression, matrices, and sandwich package.
1) In the sandwich package, I would like to better understand the
meat() function.
>From the bread() documentation, for a simple OLS regression, bread() returns
(1/n * X'X)^(-1)
That is, for a simple reg
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