Is there any way to perform stepwise Poisson regression with GEE in R?
(I know, and agree that stepwise procedures are not best statistical practice.
I have developed a model with 10 independent variables, my client want me to
include 50 other independent variables. I, of course, don't what to do
On Sun, 14 Oct 2012, Eiko Fried wrote:
Thank you for the detailed answer, that was really helpful. I did some
excessive reading and calculating in the last hours since your reply,
and have a few (hopefully much more informed) follow up questions.
1) In the vignette("countreg", package = "pscl
just a side note for your 4th question.
for a small sample, clarke test instead of vuong test might be more
appropriate and the calculation is so simple that even excel can
handle it :-)
On Sun, Oct 14, 2012 at 12:00 PM, Eiko Fried wrote:
> I would like to test in R what regression fits my data
Hi Eiko,
This is not a direct response to your question, but I thought you
might find these pages helpful:
On negative binomial:
http://www.ats.ucla.edu/stat/R/dae/nbreg.htm
and zero inflated poisson:
http://www.ats.ucla.edu/stat/R/dae/zipoisson.htm
In general this page lists a variety of dif
Thank you for the detailed answer, that was really helpful.
I did some excessive reading and calculating in the last hours since your
reply, and have a few (hopefully much more informed) follow up questions.
1) In the vignette("countreg", package = "pscl"), LLH, AIC and BIC values
are listed for
On Sun, 14 Oct 2012, Eiko Fried wrote:
I would like to test in R what regression fits my data best. My dependent
variable is a count, and has a lot of zeros.
And I would need some help to determine what model and family to use
(poisson or quasipoisson, or zero-inflated poisson regression), and
I would like to test in R what regression fits my data best. My dependent
variable is a count, and has a lot of zeros.
And I would need some help to determine what model and family to use
(poisson or quasipoisson, or zero-inflated poisson regression), and how to
test the assumptions.
1) Poisson R
Jim Silverton gmail.com> writes:
>
> Is there R code that can do a Poisson Regression and plot the density of the
> fitted values?
>
?glm
glm(...,family=poisson)
?predict.glm
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Is there R code that can do a Poisson Regression and plot the density of the
fitted values?
--
Thanks,
Jim.
[[alternative HTML version deleted]]
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PLEASE do read t
The other possibilities are:
(1) you're missing a necessary interaction term
(2) one of the variables affecting output just isn't in your data set.
(3) you need to transform 'hosp_days' or 'age' -- are those the only
two continuous variables? Might be worth trying to plot 'em versus #
of hospital
Hello All,
I have a question regarding using Poission Regression, I would like to Model
the number of hospitalizations by a set of covariates.
The issue I ran into is "lack of fit" even after I tried to solve the
"overdispersion" problem with negetive binomial.
What would you suggest?
1. Is t
On 10/14/2010 06:42 PM, Viechtbauer Wolfgang (STAT) wrote:
> Since the number of parameters then rises linearly with the number of
> subjects, this may be a case where maximum likelihood theory breaks
> down, that is, a Neyman-Scott problem.
My thought too. The basic structure is close to the Rasc
ael Friendly
Sent: Thursday, October 14, 2010 14:56 To: Antonio Paredes
Cc: r-help@r-project.org
Subject: Re: [R] Poisson Regression
> On 10/13/2010 4:50 PM, Antonio Paredes wrote:
>> Hello everyone,
>>
>> I wanted to ask if there is an R-package to fit the following
>> Pois
On 10/13/2010 4:50 PM, Antonio Paredes wrote:
Hello everyone,
I wanted to ask if there is an R-package to fit the following Poisson
regression model
log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
i=1,\cdots,N (subjects)
j=0,1 (two levels)
k=0,1 (two levels)
treating the \phi_{i} as nui
On Wed, 13 Oct 2010, David Winsemius wrote:
On Oct 13, 2010, at 4:50 PM, Antonio Paredes wrote:
Hello everyone,
I wanted to ask if there is an R-package to fit the following Poisson
regression model
log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
i=1,\cdots,N (subjects)
j=0,1 (two le
Thursday, 14 October 2010 10:22 AM
To: Antonio Paredes
Cc: r-help@r-project.org
Subject: Re: [R] Poisson Regression
On Oct 13, 2010, at 4:50 PM, Antonio Paredes wrote:
> Hello everyone,
>
> I wanted to ask if there is an R-package to fit the following Poisson
> regression model
>
&g
On Oct 13, 2010, at 4:50 PM, Antonio Paredes wrote:
Hello everyone,
I wanted to ask if there is an R-package to fit the following Poisson
regression model
log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
i=1,\cdots,N (subjects)
j=0,1 (two levels)
k=0,1 (two levels)
treating the \phi_{i
Hello everyone,
I wanted to ask if there is an R-package to fit the following Poisson
regression model
log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
i=1,\cdots,N (subjects)
j=0,1 (two levels)
k=0,1 (two levels)
treating the \phi_{i} as nuinsance parameters.
Thank you very much
--
-Ton
Peter,
I should have added that because I have over dispersion, I am ruining a
quasipoisson regression.
John
John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GR
John Sorkin wrote:
windows XP
R 2.10
When computing the variance of a linear combination of the
coefficients from a Poisson regression (i.e. glm with log link and
offset) should one use the scaled or unscaled covariance matrix? For a
simple linear regression (i.e. lm), I believe we use the u
windows XP
R 2.10
When computing the variance of a linear combination of the coefficients from a
Poisson regression (i.e. glm with log link and offset) should one use the
scaled or unscaled covariance matrix? For a simple linear regression (i.e. lm),
I believe we use the unscaled matrix; for Po
On Sat, 19 Sep 2009, Axel Urbiz wrote:
Hi All,
My dependent variable is a ratio that takes a value of 0 (zero) for 95% of
the observations and positive non-integer values for the other 5%. What
model would be appropriate? I'm thinking of fitting a GLM with a Poisson ~.
Now, becuase it takes non
Hi All,
My dependent variable is a ratio that takes a value of 0 (zero) for 95% of
the observations and positive non-integer values for the other 5%. What
model would be appropriate? I'm thinking of fitting a GLM with a Poisson ~.
Now, becuase it takes non-integer values, using the glm function wi
Dear Goran,
I need to find an EM algorithm for finding the MLE estimates for a
negative binomial distribution.
I saw your web post on the R help network and wonder if you might be
willing to share your code or algorithm.
It would be greatly appreciated.
Thank you,
Dave LeBlond
Sr. Statistici
On 13-May-08 14:25:37, Michael Dewey wrote:
> At 00:56 09/05/2008, Ted Harding wrote:
>>I'd like to thank Paul Johnson and Achim Zeileis heartily
>>for their thorough and accurate responses to my query.
>>
>>I think that the details og how to use the procedure, and
>>of its variants, which they hav
At 00:56 09/05/2008, Ted Harding wrote:
I'd like to thank Paul Johnson and Achim Zeileis heartily
for their thorough and accurate responses to my query.
I think that the details og how to use the procedure, and
of its variants, which they have sent to the list should
be definitive -- and very he
I'd like to thank Paul Johnson and Achim Zeileis heartily
for their thorough and accurate responses to my query.
I think that the details og how to use the procedure, and
of its variants, which they have sent to the list should
be definitive -- and very helpfully usable -- for folks
like myself wh
Once again, Paul, many thanks for your thorough examination
of this question! And for spelling out your approach!!!
It certainly looks as though you're very close to target
(or even spot-on).
I've only one comment -- see at end.
On 08-May-08 20:35:38, Paul Johnson wrote:
> Ted Harding said:
>> I
Paul & Ted:
> > I can get the estimated RRs from
>
> > RRs <- exp(summary(GLM)$coef[,1])
>
> > but do not see how to implement confidence intervals based
> > on "robust error variances" using the output in GLM.
>
>
> Thanks for the link to the data. Here's my best guess. If you use
> the follow
Ted Harding said:
> I can get the estimated RRs from
> RRs <- exp(summary(GLM)$coef[,1])
> but do not see how to implement confidence intervals based
> on "robust error variances" using the output in GLM.
Thanks for the link to the data. Here's my best guess. If you use
the following approac
On Thu, May 8, 2008 at 8:38 AM, Ted Harding
<[EMAIL PROTECTED]> wrote:
> The below is an old thread:
>
> On 02-Jun-04 10:52:29, Lutz Ph. Breitling wrote:
> > Dear all,
> >
> > i am trying to redo the 'eyestudy' analysis presented on the site
> > http://www.ats.ucla.edu/stat/stata/faq/relative_
The below is an old thread:
On 02-Jun-04 10:52:29, Lutz Ph. Breitling wrote:
> Dear all,
>
> i am trying to redo the 'eyestudy' analysis presented on the site
> http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm
> with R (1.9.0), with special interest in the section on "relative
> risk esti
glmstat wrote:
> I have these questions:
> (1) Use Poisson regression to estimate the main effects of car, age, and
> dist (each treated as categorical and modelled using indicator variables)
> and interaction terms.
> (2) It was determined by one study that all the interactions were
> unimportan
I have these questions:
(1) Use Poisson regression to estimate the main effects of car, age, and
dist (each treated as categorical and modelled using indicator variables)
and interaction terms.
(2) It was determined by one study that all the interactions were
unimportant and decided that age and
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