R help - Jun 2006 - Maximum likelihood estimation of Regression parameters

Hi,

I want to use Maximum likelihood to estimate the parameters from my regression
line.
I have purchased the book "Applied linear statistical models" from
Neter, Kutner, nachtsheim & Wasserman, and in one of the first chapters,
they use maximum likelihood to estimate the parameters.
Now I want to tried it for my self, but couldn't find the right function.
In the book, they give a fixed variance to work with, but I couldn't find a
function where I can estimate the predictor and where I have to give the
variance.
Or isn't this neccesairy?
Also they calculate likelihood values for the different values, used to estimate
the parameters (like a normal probability curve), is it possible to do this with
R?

Kind regards

Bart
	[[alternative HTML version deleted]]

mle(stats4)                Maximum Likelihood Estimation

is it list above what you want?

On 6/10/06, Bart Joosen <bartjoosen at hotmail.com>
wrote:
> Hi,
>
> I want to use Maximum likelihood to estimate the parameters from my
regression line.
> I have purchased the book "Applied linear statistical models"
from Neter, Kutner, nachtsheim & Wasserman, and in one of the first
chapters, they use maximum likelihood to estimate the parameters.
> Now I want to tried it for my self, but couldn't find the right
function.
> In the book, they give a fixed variance to work with, but I couldn't
find a function where I can estimate the predictor and where I have to give the
variance.
> Or isn't this neccesairy?
> Also they calculate likelihood values for the different values, used to
estimate the parameters (like a normal probability curve), is it possible to do
this with R?
>
> Kind regards
>
> Bart
>        [[alternative HTML version deleted]]
>
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>

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