Your queries appear to concern statistical issues. This list is about
R programming and related; statistical issues are typically OT here.
stats.stackexchange.com or a local statistical expert are probably
better places to seek statistical advice.
Cheers,
Bert
Bert Gunter
"The trouble with having
I am using a GAMM to model my data (this is as far as I know the only way I
can use the negative binomial distribution AND a correlation structure
within the model).
I measured animal detections (including zero detections) per hour at 3
different locations in an area. location is a factor in my mo
Achim Zeileis uibk.ac.at> writes:
>
> On Thu, 13 Mar 2014, Tim Marcella wrote:
>
> > Hi,
> >
> > I am working with hurdle models in the pscl package to model zero
> > inflated overdispersed count data and want to incorporate censored
> > observations into the equation. 33% of the observed pos
On Thu, 13 Mar 2014, Tim Marcella wrote:
Hi,
I am working with hurdle models in the pscl package to model zero
inflated overdispersed count data and want to incorporate censored
observations into the equation. 33% of the observed positive count data
is right censored, i.e. subject lost to fo
Hi,
I am working with hurdle models in the pscl package to model zero inflated
overdispersed count data and want to incorporate censored observations into
the equation. 33% of the observed positive count data is right censored,
i.e. subject lost to follow up during the duration of the study. Can t
Dear R community
I'm new to R and I'd like to ask for hints how to approach the following
problem:
I have two vectors of count data 'observed_S' and 'observed_A'. Whereas
'observed_S' follows a neg. binomial distribution but 'observed_A' is a
mixture of an unknown variable 'A' (also from a n
var(y) = mu + mu^2/theta where E(y) = mu.
best,
Simon
On 13/11/13 10:38, Mark Payne wrote:
Dear R-help,
The negative binomial distribution has several different
parameterisations, but I can't seem to figure out what the exact one
used in mgcv's negbin family is? negbin() requires a theta argum
On 13/11/2013 09:38, Mark Payne wrote:
Dear R-help,
The negative binomial distribution has several different
parameterisations, but I can't seem to figure out what the exact one
used in mgcv's negbin family is? negbin() requires a theta argument,
but its not clear anywhere in the documentation (
Dear R-help,
The negative binomial distribution has several different
parameterisations, but I can't seem to figure out what the exact one
used in mgcv's negbin family is? negbin() requires a theta argument,
but its not clear anywhere in the documentation (that I can find), how
this parameter shou
On 02/28/2013 08:27 PM, Martin Spindler wrote:
Dear all,
I would like to ask, if there is a way to make the variance / dispersion
parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function
glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate
the param
Martin Spindler gmx.de> writes:
>
> Dear all,
>
> I would like to ask, if there is a way to make the
> variance / dispersion parameter $\theta$ (referring to
> MASS, 4th edition, p. 206) in the function glm.nb dependent on the
> data, e.g. $1/ \theta = exp(x \beta)$ and
> to estimate the param
On 28/02/2013 07:27, Martin Spindler wrote:
Dear all,
I would like to ask, if there is a way to make the variance / dispersion
parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function
glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate
the paramete
Dear all,
I would like to ask, if there is a way to make the variance / dispersion
parameter $\theta$ (referring to MASS, 4th edition, p. 206) in the function
glm.nb dependent on the data, e.g. $1/ \theta = exp(x \beta)$ and to estimate
the parameter vector $\beta$ additionally.
If this is not
Hi Raeanne,
gamm fits using PQL, which doesn't always converge, however looking at
your data, a couple of things stand out...
1. Trying a Tweedie seemed to give nicer residual plots than the
negative binomial, and also converges with gamm. e.g.
SB.gam3<-gam(count~offset(vol_offset)+
s(Dep
gamm can't estimate the theta parameter for a negative binomial
automatically. It can only work with fixed user supplied values for
theta (i.e. negbin(2.3) should work, but negbin(c(1,10)) won't). Is
negative binomial the only thing you can use here (it doesn't seem like
the most natural choice
Hello,
I am having some difficulty converting my gam code to a correct gamm code, and
I'm really hoping someone will be able to help me.
I was previously using this script for my overdispersed gam data:
M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+
humidity, fam
Hi all,
I am using the negative binomila glm in MASS. This is my data alled data1:
Reps Treats counts
HSM11 0 21
HSM22 0 34
HSM33 0 27
PTM11 1 32
PTM22 1 20
PTM33 1 23
I goal is to do a GLM to extract the p-value
Hello,
I have some data that exhibits a negative binomial distribution and also
spatial structure. I would like to create a model that accounts for both.
However, instead of locations, I have a distance matrix (cost matrix)
describing the spatial relationships among the locations. I have tried usi
Hello list,
Has anyone had any luck creating an M-step driver for negative
binomial regression for use with package flexmix? I've had a look
here: http://cran.r-project.org/web/packages/flexmix/vignettes/flexmix-intro.pdf
as well as poking around in the flexmix source, but I haven't had much
luck
I am a student who is doing empirical work for his thesis and trying to
switch to R. I am familiar with Stata, and at the moment I am trying to
replicate some of my previous work.
I have a large unbalanced panel data set, observations for different
countries between 1970 and 2007. My dependent
On Jan 5, 2010, at 12:20 PM, David Winsemius wrote:
On Jan 5, 2010, at 9:38 AM, Stefani Mallia wrote:
Hi,
I'm trying to fit a glm with a negative binomial error distribution
and a log link, using the example found in the paper Stochastic
Claims Reserving In General Insurance by England
On Jan 5, 2010, at 9:38 AM, Stefani Mallia wrote:
Hi,
I'm trying to fit a glm with a negative binomial error distribution
and a log link, using the example found in the paper Stochastic
Claims Reserving In General Insurance by England and Verrall.
I am attaching a pdf since it is more di
Marty Kardos wrote:
>
> Hi;
>
> I am running generalized linear mixed models (GLMMs) with the lmer
> function
> from the lme4 package in R 2.6.2. My response variable is overdispersed,
> and
> I would like (if possible) to run a negative binomial GLMM with lmer if
> possible. I saw a posting
Perhaps because you have not loaded the package that contains it?
If you have installed MASS (via the super package VR) then try:
require(MASS)
?negative.binomial
--
David Winsemius
On Dec 11, 2008, at 4:29 PM, Marty Kardos wrote:
Hi;
I am running generalized linear mixed models (GLMMs)
Hi;
I am running generalized linear mixed models (GLMMs) with the lmer function
from the lme4 package in R 2.6.2. My response variable is overdispersed, and
I would like (if possible) to run a negative binomial GLMM with lmer if
possible. I saw a posting from November 15, 2007 which indicated tha
I estimated a negative binomial model using zelig.
z.out<- zelig(NEWBHC~ PW80 + CHNGBLK + XBLK,data=data, model="negbin")
How do I calculate predicted probabilities for this model? Is it the same
process as a poisson regression?
Thanks in advance
Joe
[[alternative HTML version deleted]]
On Wed, 1 Oct 2008, Donald Catanzaro, PhD wrote:
Good Day All,
I have a negative binomial model which I have developed using the MASS
library. I now would like to develop some predictions from it.
Running the predict.glm (stats library) using type="response" gives me a
non-integer value whi
Good Day All,
I have a negative binomial model which I have developed using the MASS
library. I now would like to develop some predictions from it.
Running the predict.glm (stats library) using type="response" gives me a
non-integer value which was rather puzzling. I would like to confirm
Hello r-help,
As the title suggests, I'm attempting to fit a negative binomial GLM
with a fixed dispersion parameter.
Both glm.nb() and glm(..., family=negative.binomial(theta, ...)) (using
MASS) do not appear to allow this; upon specifying a value for theta,
each then proceeds to re-estimate it.
Prof Brian Ripley wrote:
'poisson' _family_, I presume?
oops, yes.
__
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 commente
On Tue, 29 Jul 2008, Ben Bolker wrote:
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Prof Brian Ripley wrote:
| On Tue, 29 Jul 2008, Ben Bolker wrote:
|
|> jcarmichael gmail.com> writes:
|>>
|>> Hello.
|>>
|>> I am attempting to duplicate a negative binomial regression in R.
|>> SAS uses
|>> g
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Prof Brian Ripley wrote:
| On Tue, 29 Jul 2008, Ben Bolker wrote:
|
|> jcarmichael gmail.com> writes:
|>
|>>
|>>
|>> Hello.
|>>
|>> I am attempting to duplicate a negative binomial regression in R.
|>> SAS uses
|>> generalized estimating equations fo
On Tue, 29 Jul 2008, Ben Bolker wrote:
jcarmichael gmail.com> writes:
Hello.
I am attempting to duplicate a negative binomial regression in R. SAS uses
generalized estimating equations for model fitting in the GENMOD procedure.
proc genmod data=mydata (where=(gender='F'));
by agegroup;
c
jcarmichael gmail.com> writes:
>
>
> Hello.
>
> I am attempting to duplicate a negative binomial regression in R. SAS uses
> generalized estimating equations for model fitting in the GENMOD procedure.
>
> proc genmod data=mydata (where=(gender='F'));
> by agegroup;
> class id gender type;
>
Hello.
I am attempting to duplicate a negative binomial regression in R. SAS uses
generalized estimating equations for model fitting in the GENMOD procedure.
proc genmod data=mydata (where=(gender='F'));
by agegroup;
class id gender type;
model count = var1 var2 var3 /dist=NB link=log offset=l
lto:[EMAIL PROTECTED]
Sent: Wednesday, May 14, 2008 2:38 AM
To: r-help@r-project.org
Cc: Mike Ryckman
Subject: Re: [R] Negative Binomial Model
On 14-May-08 09:11:31, Achim Zeileis wrote:
On Wed, 14 May 2008, Mike Ryckman wrote:
Hello,
I am trying to run a negative binomial regression model in R
08 2:38 AM
To: r-help@r-project.org
Cc: Mike Ryckman
Subject: Re: [R] Negative Binomial Model
On 14-May-08 09:11:31, Achim Zeileis wrote:
> On Wed, 14 May 2008, Mike Ryckman wrote:
>
>> Hello,
>>
>> I am trying to run a negative binomial regression model in R and
>> ca
On 14-May-08 09:11:31, Achim Zeileis wrote:
> On Wed, 14 May 2008, Mike Ryckman wrote:
>
>> Hello,
>>
>> I am trying to run a negative binomial regression model in R and
>> can't get the standard errors to match the output I get from the
>> Stata nbreg command. I've tried a few different options b
On Wed, 14 May 2008, Mike Ryckman wrote:
Hello,
I am trying to run a negative binomial regression model in R and can't get
the
standard errors to match the output I get from the Stata nbreg command. I've
tried a few different options but haven't had much luck. The closest I've
found
is:
gamlss
Hello,
I am trying to run a negative binomial regression model in R and can't get
the
standard errors to match the output I get from the Stata nbreg command. I've
tried a few different options but haven't had much luck. The closest I've
found
is:
gamlss(formula, family = NBI, sigma.formula = ~ 1,
-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of H. Skaug
Sent: Thursday, 15 November 2007 8:16 PM
To: r-help@r-project.org
Subject: [R] negative binomial lmer
If lmer() does not do it, you can try:
http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html
It handles negative
If lmer() does not do it, you can try:
http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html
It handles negative binomial responce (but you may have to remove data
entries involving NA manually).
Regards,
hans
>Hi
>I am running an lmer which works fine with family=poisson
>
>mixed.mode
Hi
I am running an lmer which works fine with family=poisson
mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit)
But it is overdispersed. I tried using family=quasipoisson but get no P
values. This didnt worry me too much as i thin
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