[R] Maximum likelihood estimation of Regression parameters

[Spencer Graves]

	  Have you looked at "lm"?  I think that's what you want.

	  Also, have you reviewed the "Documentation" list at 
"www.r-project.org"?  Neter, Kutner, nachtsheim & Wasserman has had a 
long and successful run having first appeared in 1974 and having gone 
through several editions since then.  However, it apparently has not 
kept up with the R revolution, and I would not recommend it today.

	  Beyond this, if you don't have Venables and Ripley (2002) Modern 
Applied Statistics with S (Springer), I recommend you look at it first. 
  It has numerous index entries on "regression" and is all around my 
favorite book on R generally.

	  Also, have you checked the "Documentation" briefly outlined at 
'www.r-project.org', including the "Contributed Documentation" on CRAN, 
and "Practical Regression and Anova using R" by Faraway in particular?

	  hope this helps.
	  Spencer Graves

Xiaoting Hua wrote:
> 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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