On Sat, 11 Jun 2005, John Fox wrote:
Dear Marc,
I get the same results -- same coefficients, standard errors, and fitted
probabilities -- from multinom() and glm(). It's true that the deviances
differ, but they, I believe, are defined only up to an additive constant:
Yes. There are many
Thanks for your response.
OK, multinom() is a more logical in this context.
But similar problem occurs:
Let these data to be analyzed using classical glm with binomial error:
m f factor m theo f theo
-Ln L model-Ln L full interecept
f
10 12 1.2
PROTECTED] On Behalf Of Marc Girondot
Sent: Saturday, June 11, 2005 3:06 AM
To: John Fox
Cc: r-help@stat.math.ethz.ch
Subject: [R] Problem with multinom ?
Thanks for your response.
OK, multinom() is a more logical in this context.
But similar problem occurs:
Let these data
: Saturday, June 11, 2005 3:06 AM
To: John Fox
Cc: r-help@stat.math.ethz.ch
Subject: [R] Problem with multinom ?
Thanks for your response.
OK, multinom() is a more logical in this context.
But similar problem occurs:
Let these data to be analyzed using classical glm with binomial error:
m f
On Sat, 11 Jun 2005, John Fox wrote:
Dear Marc,
I get the same results -- same coefficients, standard errors, and fitted
probabilities -- from multinom() and glm(). It's true that the deviances
differ, but they, I believe, are defined only up to an additive constant:
Yes. There are many
Dear Marc,
I get the same results -- same coefficients, standard errors, and fitted
probabilities -- from multinom() and glm(). It's true that the deviances
differ, but they, I believe, are defined only up to an additive constant:
predict(dt.b, type=response)
1 2 3
Dear Marc,
-Original Message-
From: Marc Girondot [mailto:[EMAIL PROTECTED]
Sent: Saturday, June 11, 2005 2:16 PM
To: John Fox
Cc: [EMAIL PROTECTED]
Subject: RE: [R] Problem with multinom ?
Dear Marc,
I get the same results -- same coefficients, standard errors, and
fitted
