Displaying 15 results from an estimated 15 matches similar to: "How to use quasibinomial?"
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
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I've originally made 48 GLM binomial models and compare the AIC values.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
2006 May 10
1
Allowed quasibinomial links (PR#8851)
Full_Name: Henric Nilsson
Version: 2.3.0 Patched (2006-05-09 r38014)
OS: Windows 2000 SP4
Submission from: (NULL) (83.253.9.137)
When supplying an unavailable link to `quasibinomial', the error message looks
strange. E.g.
> quasibinomial("x")
Error in quasibinomial("x") : 'x' link not available for quasibinomial family,
available links are "logit",
2009 Nov 24
1
overdispersion and quasibinomial model
I am looking for the correct commands to do the following things:
1. I have a binomial logistic regression model and i want to test for
overdispersion.
2. If I do indeed have overdispersion i need to then run a quasi-binomial
model, but I'm not sure of the command.
3. I can get the residuals of the model, but i need to then apply a shapiro
wilk test to test them. Does anyone know the command
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
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
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
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
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
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
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.
2008 May 14
1
rlm and lmrob error messages
Hello all,
I'm using R2.7.0 (on Windows 2000) and I'm trying do run a robust
regression on following model structure:
model = "Y ~ x1*x2 / (x3 + x4 + x5 +x6)"
where x1 and x2 are both factors (either 1 or 0) and x3.....x6 are numeric.
The error code I get when running rlm(as.formula(model), data=daymean) is:
error in rlm.default(x, y, weights, method = method, wt.method =
2013 Apr 22
7
Multiple lon lat points in the map with ggplot2
Hello R users,
For the last few days I am struggling with the following task:
my data.frame:
A1 A2 A3 B1 B2
B3
58.81 53.292 54.501 13.013 17.39 19.407 56.02 56.251 54.033 20.099 13.15
10.411 55.376 53.099 57.625 13.396 21.031 13.22 58.584 53.194 54.218
13.038 16.854 19.289 55.7 55.921 53.847 19.942 13.153 9.828 55.093 52.934
2003 Jul 04
1
Quasi AIC
Dear all,
Using the quasibinomial and quasipoisson families results in no AIC being
calculated. However, a quasi AIC has actually been defined by Lebreton et al
(1992). In the (in my opinon, at least) very interesting book by Burnham and
Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something
that could be implemented in R.
Take a look at page 23 in this pdf: