Dear All,
I've been using step function to find me the best model.this basically works
by using AIC score fucntion that is implemented on step(). The problem I'm
facing with lots of variables on the model for example :
step(lm(x1~x2,x3,x4,..x13)) sometimes gives me a warning message which
is
Choosing the model with minimum AIC is just one consideration that
might be used. If you look at books such as McQuarrie and Tsai
(1998), Regression and Time Series Model Selection, World Scientific,
you will find about 450 pages dealing mainly with the use of this and
similar criteria to select a
John,
Thank you for those references.
Cheers,
Michael
On Tue, Oct 14, 2008 at 8:27 AM, John C Frain <[EMAIL PROTECTED]> wrote:
> Choosing the model with minimum AIC is just one consideration that
> might be used. If you look at books such as McQuarrie and Tsai
> (1998), Regression and Time Seri
Michael Just gmail.com> writes:
> I ran AIC for some competing models I created. I get df and an AIC score
> from the AIC procedure. Can I use the models with the lowest AIC scores from
> this procedure to choose my 'best' models?
Depends. You told us nothing. Have you tried to search the list
Hello,
I ran AIC for some competing models I created. I get df and an AIC score
from the AIC procedure. Can I use the models with the lowest AIC scores from
this procedure to choose my 'best' models? If not, what else do I need to do
(and know) and how can I do it in R to chose the 'best' models?
5 matches
Mail list logo
