Re: [R] Estimating QAIC using glm with the quasibinomial family
I am glad to have joined such an active online community! I assume from your
R code that the deviance reported by R for "quasi..." families is not
"quasi-deviance" and that the following function summarizes the calculation
(note that I have added a parameter to k to account for the estimation of
dispersion as suggested by Burnham and Anderson 2002).
#
# Determine the dispersion coefficient from the global model constructed
with all of the
# predictors being considered (Burnham and Anderson 2002, p.68).
#
c_hat=summary(model.global)$dispersion
#
QAIC=function(model.candidate,c_hat) {
k=length(model.candidate$coefficients)+1 # add 1 for the estimate of
c_hat
(model.candidate$deviance/c_hat)+2*k
}
Thank you again for your correspondance and assistance.
Sincerely,
Darren Gillis
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Re: [R] Estimating QAIC using glm with the quasibinomial family
R-users
E-mail: r-help@r-project.org
>but I was specificly interested in calculating QAIC and QAICc from
>a glm fitted with the "family=quasibinomial" option.
If you use "family=quasibinomial(link = "logit")" in glm(),
the program will be:
function ()
{
xx <- c(1,2,3,4,5,6,7,8,9,10)
yy <- c(1,0,1,0,0,1,0,1,1,1)
data1 <- data.frame(x=xx, y=yy)
out1 <- glm(y~x, data=data1, family=quasibinomial(link = "logit"))
print(out1)
aic0 <- out1$aic
print("aic0")
print(aic0)
dev1 <- out1$deviance
aic1 <- dev1+ 2*2
print("aic1")
print(aic1)
c1 <- 2.5
qaic1 <- dev1/c1+ 2*2
print("qaic1")
print(qaic1)
}
The result is:
Call: glm(formula = y ~ x, family = quasibinomial(link = "logit"),
data = data1)
Coefficients:
(Intercept)x
-0.7300 0.2131
Degrees of Freedom: 9 Total (i.e. Null); 8 Residual
Null Deviance: 13.46
Residual Deviance: 12.63AIC: NA
[1] "aic0"
[1] NA
[1] "aic1"
[1] 16.63054
[1] "qaic1"
[1] 9.052216
As you pointed out, glm() does not output the value of either AIC or
QAIC.
You are supposed to calculate those values. I guess that it is because AIC
is not
appropriate for this case, and the concept of QAIC has not been authorized
yet.
K. Takezawa
[[alternative HTML version deleted]]
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Re: [R] Estimating QAIC using glm with the quasibinomial family
Thank you for the rapid response. I have no trouble in following your
example, but I was specificly interested in calculating QAIC and QAICc from
a glm fitted with the "family=quasibinomial" option. This provides an
estimate of dispersion, but does not contain an explicit value for AIC or
log-likelihood. Do you have any insights into how to deal with the output
from this specific option? Or are you suggesting not to use
family=quasibinomial when faced with potential overdispersion? Regardless,
thank you again for your response.
Cheers, Darren
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Kunio takezawa
Sent: Tuesday, May 06, 2008 8:41 PM
To: r-help@r-project.org
Subject: Re: [R] Estimating QAIC using glm with the quasibinomial family
R-users
E-mail: r-help@r-project.org
>My question is: Will this calculation be valid with the residual
>deviance returned by the glm() function using the quasibinomial family
>as reported in R?
Let me show you a simple example, assuming c=2.5:
function ()
{
xx <- c(1,2,3,4,5,6,7,8,9,10)
yy <- c(1,0,1,0,0,1,0,1,1,1)
data1 <- data.frame(x=xx, y=yy)
out1 <- glm(y~x, data=data1, family=binomial)
print(out1)
aic0 <- out1$aic
print("aic0")
print(aic0)
dev1 <- out1$deviance
aic1 <- dev1+ 2*2
print("aic1")
print(aic1)
c1 <- 2.5
qaic1 <- dev1/c1+ 2*2
print("qaic1")
print(qaic1)
}
The result is:
Call: glm(formula = y ~ x, family = binomial, data = data1)
Coefficients:
(Intercept)x
-0.7300 0.2131
Degrees of Freedom: 9 Total (i.e. Null); 8 Residual
Null Deviance: 13.46
Residual Deviance: 12.63AIC: 16.63
[1] "aic0"
[1] 16.63054
[1] "aic1"
[1] 16.63054
[1] "qaic1"
[1] 9.052216
I hope that this R program will be of some help to you.
K. Takezawa
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
__
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Re: [R] Estimating QAIC using glm with the quasibinomial family
R-users
E-mail: r-help@r-project.org
>My question is: Will this calculation be valid with the residual deviance
>returned by the glm() function using the quasibinomial family as
>reported in R?
Let me show you a simple example, assuming c=2.5:
function ()
{
xx <- c(1,2,3,4,5,6,7,8,9,10)
yy <- c(1,0,1,0,0,1,0,1,1,1)
data1 <- data.frame(x=xx, y=yy)
out1 <- glm(y~x, data=data1, family=binomial)
print(out1)
aic0 <- out1$aic
print("aic0")
print(aic0)
dev1 <- out1$deviance
aic1 <- dev1+ 2*2
print("aic1")
print(aic1)
c1 <- 2.5
qaic1 <- dev1/c1+ 2*2
print("qaic1")
print(qaic1)
}
The result is:
Call: glm(formula = y ~ x, family = binomial, data = data1)
Coefficients:
(Intercept)x
-0.7300 0.2131
Degrees of Freedom: 9 Total (i.e. Null); 8 Residual
Null Deviance: 13.46
Residual Deviance: 12.63AIC: 16.63
[1] "aic0"
[1] 16.63054
[1] "aic1"
[1] 16.63054
[1] "qaic1"
[1] 9.052216
I hope that this R program will be of some help to you.
K. Takezawa
[[alternative HTML version deleted]]
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[R] Estimating QAIC using glm with the quasibinomial family
Hello R-list. I am a "long time listener - first time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the quasibinomial family. I found a general reference suggesting a simple solution: "we calculated QAICc adjusting for overdispersion by dividing the residual deviance (i.e. -2 loglikelihood) with the overdispersion parameter calculated from the most complex model as the sum of squares Pearson residuals divided by the number of degrees of freedom (Burnham & Anderson, 2002). " - Mystrud et al. 2007. Animal Conservation. 10:77-87. My question is: Will this calculation be valid with the residual deviance returned by the glm() function using the quasibinomial family as reported in R? I thought I should ask to be certain that there is no dispersion correction applied to the reported deviance, as encouraged by Burnham and Anderson, 2nd ed., 2002 on p.69: "When data are overdispersed and c > 1, the proper likelihood is log(L)/c". Regards, Darren Gillis Department of Biological Sciences Faculty of Science University of Manitoba Winnipeg, MB Canada, R3T 2N2 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
