Dear Paul
Here are some attempts at your questions. I hope it's of some help.
On Tuesday, Mar 16, 2004, at 06:00 Europe/London, Paul Johnson wrote:
Greetings, everybody. Can I ask some glm questions?
1. How do you find out -2*lnL(saturated model)?
In the output from glm, I find:
Null
David Firth wrote (in response to a question from Paul Johnson):
On the more general point: yes, if all that students need to know is
OLS, Poisson rate models and logistic regression, then GLM is overkill.
I couldn't agree less. The glm (not GLM!) framework gives a
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of David Firth
Sent: Tuesday, March 16, 2004 1:12 PM
To: Paul Johnson
Cc: [EMAIL PROTECTED]
Subject: Re: [R] glm questions
Dear Paul
Here are some attempts at your questions. I hope it's of some
On Tuesday, Mar 16, 2004, at 14:51 Europe/London, BXC (Bendix
Carstensen) wrote:
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of David Firth
Sent: Tuesday, March 16, 2004 1:12 PM
To: Paul Johnson
Cc: [EMAIL PROTECTED]
Subject: Re: [R] glm questions
Dear
BXC (Bendix Carstensen) [EMAIL PROTECTED] writes:
It's important to remember that lnL is defined only up to an additive
constant. For example a Poisson model has lnL contributions -mu +
y*log(mu) + constant, and the constant is arbitrary. The
differencing
in the deviance
On Tue, 16 Mar 2004, David Firth wrote:
I am unclear what you are asking here. I assume by scaled deviance
you mean deviance divided by phi, a (known) scale parameter? (I'm
sorry, I don't know SAS's definition.)In many applications (eg
binomial, Poisson) deviance and scaled deviance are
On Tuesday, Mar 16, 2004, at 15:15 Europe/London, Rolf Turner wrote:
David Firth wrote (in response to a question from Paul Johnson):
On the more general point: yes, if all that students need to know is
OLS, Poisson rate models and logistic regression, then GLM is
overkill.
I couldn't agree
I'm confused going back and forth between the textbooks and these
emails. Please pardon me that I seem so pedantic.
I am pretty certain that -2lnL(saturated) is not 0 by definition. In
the binomial model with groups of size=1, then the observed scores will
be {0,1} but the predicted mean
On Tuesday, Mar 16, 2004, at 20:28 Europe/London, Paul Johnson wrote:
I'm confused going back and forth between the textbooks and these
emails. Please pardon me that I seem so pedantic.
I am pretty certain that -2lnL(saturated) is not 0 by definition.
I'm pretty certain of that too.
In the
Greetings, everybody. Can I ask some glm questions?
1. How do you find out -2*lnL(saturated model)?
In the output from glm, I find:
Null deviance: which I think is -2[lnL(null) - lnL(saturated)]
Residual deviance: -2[lnL(fitted) - lnL(saturated)]
The Null model is the one that includes the
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