Displaying 20 results from an estimated 1000 matches similar to: "Allowed quasibinomial links (PR#8851)"
2003 Jul 03
1
How to use quasibinomial?
Dear all,
I've got some questions, probably due to misunderstandings on my behalf, related
to fitting overdispersed binomial data using glm().
1. I can't seem to get the correct p-values from anova.glm() for the F-tests when
supplying the dispersion argument and having fitted the model using
family=quasibinomial. Actually the p-values for the F-tests seems identical to the
p-values for
2006 Jun 13
1
Slight fault in error messages
Just a quick point which may be easy to correct. Whilst typing the
wrong thing into R 2.2.1, I noticed the following error messages,
which seem to have some stray quotation marks and commas in the list
of available families. Perhaps they have been corrected in the latest
version (sorry, I don't want to upgrade yet, but it should be easy to
check)?
> glm(1 ~ 2,
2006 Jan 14
2
initialize expression in 'quasi' (PR#8486)
This is not so much a bug as an infelicity in the code that can easily
be fixed.
The initialize expression in the quasi family function is, (uniformly
for all links and all variance functions):
initialize <- expression({
n <- rep.int(1, nobs)
mustart <- y + 0.1 * (y == 0)
})
This is inappropriate (and often fails) for variance function
"mu(1-mu)".
2012 Feb 07
1
binomial vs quasibinomial
After looking at 48 glm binomial models I decided to try the quasibinomial
with the top model 25 (lowest AIC). To try to account for overdispersion
(residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is
the same for the quasibinomial and less sectors of the beach were
significant by p-value. While the p-values in the binomial were more
significant for each section of the
2009 Mar 02
2
Unrealistic dispersion parameter for quasibinomial
I am running a binomial glm with response variable the no of mites of two
species y->cbind(mitea,miteb) against two continuous variables (temperature
and predatory mites) - see below. My model shows overdispersion as the
residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial
to account for overdispersion the dispersion parameter estimate is 2501139,
which seems
2009 Oct 02
1
confint fails in quasibinomial glm: dims do not match
I am unable to calculate confidence intervals for the slope estimate in a
quasibinomial glm using confint(). Below is the output and the package info
for MASS. Thanks in advance!
R 2.9.2
MASS 7.2-48
> confint(glm.palive.0.str)
Waiting for profiling to be done...
Error: dims [product 37] do not match the length of object [74]
> glm.palive.0.str
Call: glm(formula = cbind(alive, red) ~ str,
2008 May 07
2
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
2008 Sep 16
1
Using quasibinomial family in lmer
Dear R-Users,
I can't understand the behaviour of quasibinomial in lmer. It doesn't
appear to be calculating a scaling parameter, and looks to be reducing the
standard errors of fixed effects estimates when overdispersion is present
(and when it is not present also)! A simple demo of what I'm seeing is
given below. Comments appreciated?
Thanks,
Russell Millar
Dept of Stat
U.
2010 Jul 26
2
modelos mixtos con familia quasibinomial
Hola a tod en s,
mi compañero y yo intentamos ver la correlación de nuestros datos
mediante regresiones logísticas. Trabajamos con proporciones (1
variable dependiente y 1 independiente) mediante modelos mixtos (los
datos están agrupados porque hay pseudoreplicación). Hemos usado el
paquete "lme4" y la función "lmer". Encontramos "overdispersion" en el
resultado
2008 Sep 09
1
binomial(link="inverse")
this may be a better question for r-devel, but ...
Is there a particular reason (and if so, what is it) that
the inverse link is not in the list of allowable link functions
for the binomial family? I initially thought this might
have something to do with the properties of canonical
vs non-canonical link functions, but since other link functions
(probit, cloglog, cauchit, log) are allowed, I
2003 Jun 19
2
Grouping binary data
Dear all,
I'm analyzing a binary outcome using glm() with a binomial distribution and
a logit link, and have now reached the point where I'd like to do some
model checking. Since my data are in binary form I'd like to collapse over
the cross-classification of the factors before the model checking.
Are there any nice and simple ways doing this? If so, how? If not, I'd be
2007 Nov 10
1
polr() error message wrt optim() and vmmin
Hi,
I'm getting an error message using polr():
Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) :
initial value in 'vmmin' is not finite
The outcome variable is ordinal and factored, and the independant variable
is continuous. I've checked the source code for both polr() and optim()
and can't find any variable called
2007 Sep 19
1
lmer using quasibinomial family
Dear all, I try to consider overdispersion in a lmer model. But using
family=quasibinomial rather than family=binomial seems to change the fit but
not the result of an anova test. In addition if we specify test="F" as it is
recomanded for glm using quasibinomial, the test remains a Chisq test. Are
all tests scaled for dispersion, or none? Why is there a difference between
glm and lmer
2020 Apr 13
0
Poor family objects error messages
Hello,
The following code:
> binomial(identity)
Generates an error message:
Error in binomial(identity) :
link "identity" not available for binomial family; available links are ?logit?, ?probit?, ?cloglog?, ?cauchit?, ?log?
While :
> binomial("identity")
Yields an identity-binomial object that works as expected with stats::glm
The error in the first example mislead
2012 Feb 07
0
GLM Quasibinomial - 48 models
I've originally made 48 GLM binomial models and compare the AIC values. But
dispersion was very large:
Example: Residual deviance: 8811.6 on 118 degrees of freedom
I was suggested to do a quasibinomial afterwards but found that it did not
help the dispersion factor of models and received a warning:
Residual deviance: 3005.7 on 67 degrees of freedom
AIC: NA
Number of Fisher Scoring
2003 Jun 03
3
gam questions
Dear all,
I'm a fairly new R user having two questions regarding gam:
1. The prediction example on p. 38 in the mgcv manual. In order to get
predictions based on the original data set, by leaving out the 'newdata'
argument ("newd" in the example), I get an error message
"Warning message: the condition has length > 1 and only the first element
will be used in: if
2004 Sep 22
2
ordered probit and cauchit
What is the current state of the R-art for ordered probit models, and
more
esoterically is there any available R strategy for ordered cauchit
models,
i.e. ordered multinomial alternatives with a cauchy link function. MCMC
is an option, obviously, but for a univariate latent variable model
this seems
to be overkill... standard mle methods should be preferable. (??)
Googling reveals that spss
2008 Nov 20
1
glmer for cauchit link function
Dear all,
A am trying to fit a generalized linear mixed effects model with a binomial
link function, my response data is binary, using the lme4 R package, for the
glmer model but with the cauchit link function (CDF of Cauchy distribution),
under the package this has not yet been coded and was wondering if anyone
knew a way in which I could incorporate this link function into the code.
Thankyou
2002 Jan 10
0
quasibinomial glm
Hello list,
i have a glm with family=binomial, link=logit but there is
over-dispersion. So, in order to take into account for this problem i
choose to do a glm with family=quasibinomial(). I'm not an expert on
this subject and i ask if someone could validate my approach (i'm not
sure for the tests) :
quasi_glm(myformula,quasibinomial(),start=mystart)
summary(quasi) # test t for
2008 Oct 26
0
LMER quasibinomial
Hi,
a while ago I posted a question regarding the use of alternative models,
including a quasibinomial mixed-effects model (see Results 1). I rerun the
exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results
2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3).
While the coefficient estimates are basically the same in all three
regressions, the estimated standard