On 30/04/2012 12:37, Jouni Helske wrote:
Dear all,
I'd like to discuss about a possible bug in function StructTS of stats
package. It seems that the function returns wrong value of the
log-likelihood, as the added constant to the relevant part of the
log-likelihood is misspecified. Here is an
On Thu, May 3, 2012 at 3:36 AM, Mark Leeds marklee...@gmail.com wrote:
Hi Ravi: As far as I know ( well , really read ) and Bert et al can say
more , the AIC is not dependent on the models being nested as long as the
sample sizes used are the same when comparing. In some cases, say comparing
Thanks, Tom, for the reply as well as to the reference to Claeskens Hjort.
Ravi
From: Thomas Lumley [tlum...@uw.edu]
Sent: Thursday, May 03, 2012 4:41 PM
To: Mark Leeds
Cc: Ravi Varadhan; r-devel@r-project.org
Subject: Re: [Rd] The constant part of the
Comparing such disparate, non-nested models can be quite problematic. I am not
sure what AIC/BIC comparisons mean in such cases. The issue of different
constants should be the least of your worries.
Ravi
-Original Message-
From: r-devel-boun...@r-project.org
Hi Ravi: As far as I know ( well , really read ) and Bert et al can say
more , the AIC is not dependent on the models being nested as long as the
sample sizes used are the same when comparing. In some cases, say comparing
MA(2), AR(1), you have to be careful with sample size usage but there is no
This is not a problem at all. The log likelihood function is a function of the
model parameters and the data, but it is defined up to an additive arbitrary
constant, i.e. L(\theta) and L(\theta) + k are completely equivalent, for any
k. This does not affect model comparisons or hypothesis
Ok, it seems that R's AIC and BIC functions warn about different constants,
so that's probably enough. The constants are not irrelevant though, if you
compute the log-likelihood of one model using StructTS, and then fit
alternative model using other functions such as arima(), which do take
account
