Displaying 20 results from an estimated 7000 matches similar to: "overdispersion + GAM"
2010 Nov 19
2
Question on overdispersion
I have a few questions relating to overdispersion in a sex ratio data set
that I am working with (note that I already have an analysis with GLMMs for
fixed effects, this is just to estimate dispersion). The response variable
is binomial because nestlings can only be male or female. I have samples of
1-5 nestlings from each nest (individuals within a nest are not independent,
so the response
2005 Jan 13
2
GAM: Remedial measures
I fitted a GAM model with Poisson distribution to a data with about 200
observations. I noticed that the plot of the residuals versus fitted values
show a trend. Residuals tend to be lower for higher fitted values. Because,
I'm dealing with count data, I'm thinking that this might be due to
overdispersion. Is there a way to account for overdispersion in any of the
packages MGCV or GAM?
2008 May 16
1
gam negative.binomial
Dear list members,
while I appreciate the possibility to deal with overdispersion for count
data either by specifying the family argument to be quasipoisson() or
negative.binomial(), it estimates just one overdispersion parameter for the
entire data set.
In my applications I often would like the estimate for overdispersion to
depend on the covariates in the same manner as the mean.
For example,
2012 Oct 18
2
Assessing overdispersion and using quasi model with lmer, possible?
Hello!
I am trying to model data on species abundance (count data) with a poisson
error distribution. I have a fixed and a random variables and thus needs a
mixed model. I strongly doubt that my model is overdispersed but I don't
know how to get the overdispersion parameter in a mixed model. Maybe someone
can help me on this point. Secondly, it seems that quasi models cannot be
implemented
2011 Jun 13
1
glm with binomial errors - problem with overdispersion
Dear all,
I am new to R and my question may be trivial to you...
I am doing a GLM with binomial errors to compare proportions of species in
different categories of seed sizes (4 categories) between 2 sites.
In the model summary the residual deviance is much higher than the degree
of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even
after correcting for overdispersion by
2003 Feb 18
4
glm and overdispersion
Hi,
I am performing glm with binomial family and my data show slight
overdispersion (HF<1.5). Nevertheless, in order to take into account for
this heterogeneity though weak, I use F-test rather than Chi-square
(Krackow & Tkadlec, 2001). But surprisingly, outputs of this two tests
are exactly similar. What is the reason and how can I scale the output
by overdispersion ??
Thank you,
2009 Feb 16
1
Overdispersion with binomial distribution
I am attempting to run a glm with a binomial model to analyze proportion
data.
I have been following Crawley's book closely and am wondering if there is
an accepted standard for how much is too much overdispersion? (e.g. change
in AIC has an accepted standard of 2).
In the example, he fits several models, binomial and quasibinomial and then
accepts the quasibinomial.
The output for residual
2011 Apr 01
1
qcc.overdispersion-test
Hi all,
I have made an overdispersion test for a data set and get the following result
Overdispersion test Obs.Var/Theor.Var Statistic p-value
poisson data 16.24267 47444.85 0
after deleting the outliers from the data set I get the following result
Overdispersion test Obs.Var/Theor.Var Statistic p-value
poisson data 16.27106 0 1
The
2008 Apr 21
1
estimate of overdispersion with glm.nb
Dear R users,
I am trying to fully understand the difference between estimating
overdispersion with glm.nb() from MASS compared to glm(..., family =
quasipoisson).
It seems that (i) the coefficient estimates are different and also (ii) the
summary() method for glm.nb suggests that overdispersion is taken to be one:
"Dispersion parameter for Negative Binomial(0.9695) family taken to be
2005 Nov 23
2
negative binomial overdispersion question
Hello,
I'm a grad student in the Intelligent Transportation Systems lab at Portland
State Univ. in Portland, OR, USA. I'm trying to learn the basics of R to run a
negative binomial in the near future, and so I ran a test regression on roadway
crash data obtained from "Statistical and Econometric Methods for
Transportation Data Analysis" by Washington et al (p. 250). I ran the
2010 Oct 25
2
Mixed-effects model for overdispersed count data?
Hi,
I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have
2006 Nov 13
1
stepAIC for overdispersed Poisson
I am wondering if stepAIC in the MASS library may be used for model
selection in an overdispersed Poisson situation. What I thought of doing
was to get an estimate of the overdispersion parameter phi from fitting
a model with all or most of the available predictors (we have a large
number of observations so this should not be problematical) and then use
stepAIC with scale = phi. Should this
2011 Aug 27
1
hopelessly overdispersed?
dear list!
i am running an anlysis on proportion data using binomial (quasibinomial
family) error structure. My data comprises of two continuous vars, body
size and range size, as well as of feeding guild, nest placement, nest
type and foragig strata as factors. I hope to model with these variables
the preference of primary forests (#successes) by certain bird species.
My code therefore looks
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
2007 Jan 11
2
overdispersion
How can I eliminate the overdispersion for binary data apart the use of the quasibinomial?
help me
Eva Iannario
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2015 Jun 25
1
Estimating overdispersion when using glm for count and binomial data
Dear All
I recently proposed a simple modification to Wedderburn's 1974 estimate
of overdispersion for count and binomial data, which is used in glm for
the quasipoisson and quasibinomial families (see the reference below).
Although my motivation for the modification arose from considering
sparse data, it will be almost identical to Wedderburn's estimate when
the data are not sparse.
2008 Oct 12
2
Overdispersion in the lmer models
Dear All,
I am working with linear mixed-effects models using the lme4 package in R. I created a model using the lmer function including some main effects, a three-way interaction and a random effect.
Because I work with a binomial and poisson distribution, I want to know whether there is overdispersion in my data or not. Does anybody know how I can retrieve this information from R?
Thank you
2009 May 18
2
Overdispersion using repeated measures lmer
Dear All
I am trying to do a repeated measures analysis using lmer and have a number
of issues. I have non-orthogonal, unbalanced data. Count data was obtained
over 10 months for three treatments, which were arranged into 6 blocks.
Treatment is not nested in Block but crossed, as I originally designed an
orthogonal, balanced experiment but subsequently lost a treatment from 2
blocks. My
2000 Apr 19
1
scale factors/overdispersion in GLM: possible bug?
I've been poking around with GLMs (on which I am *not* an expert) on
behalf of a student, particularly binomial (standard logit link) nested
models with overdispersion.
I have one possible bug to report (but I'm not confident enough to be
*sure* it's a bug); one comment on the general inconsistency that seems to
afflict the various functions for dealing with overdispersion in GLMs
2010 Dec 06
1
waldtest and nested models - poolability (parameter stability)
Dear All,
I'm trying to use waldtest to test poolability (parameter stability) between
two logistic regressions. Because I need to use robust standard errors
(using sandwich), I cannot use anova. anova has no problems running the
test, but waldtest does, indipendently of specifying vcov or not. waldtest
does not appear to see that my models are nested. H0 in my case is the the
vector of