

Hi, I' m having a hard time understanding the computation of degrees of freedom when runing nlme() on the following model:
> formula(my data.gd)
dLt ~ Lt  ID
TasavB< function(Lt, Linf, K) (K*(LinfLt))
my model.nlme < nlme (dLt ~ TasavB(Lt, Linf, K),
data = my data.gd,
fixed = list(Linf ~ 1, K ~ 1),
start = list(fixed = c(70, 0.4)),
na.action= na.include, naPattern = ~!is.na(dLt))
> summary(my model.nlme)
Nonlinear mixedeffects model fit by maximum likelihood
Model: dLt ~ TasavB(Lt, Linf, K)
Data: my data.gd
AIC BIC logLik
13015.63 13051.57 6501.814
Random effects:
Formula: list(Linf ~ 1 , K ~ 1 )
Level: ID
Structure: General positivedefinite
StdDev Corr
Linf 7.3625291 Linf
K 0.0845886 0.656
Residual 1.6967358
Fixed effects: list(Linf + K ~ 1)
Value Std.Error DF tvalue pvalue
Linf 69.32748 0.4187314 402 165.5655 <.0001
K 0.31424 0.0047690 2549 65.8917 <.0001
Standardized WithinGroup Residuals:
Min Q1 Med Q3 Max
3.98674 0.5338083 0.02783649 0.5261591 4.750609
Number of Observations: 2952
Number of Groups: 403
>
Why are the DF of Linf and K different? I would apreciate if you could point me to a reference
Note: I working with Splus 6.1. for Windows
Lic. Gabriela Escati Peñaloza
Biología y Manejo de Recursos Acuáticos
Centro Nacional Patagónico(CENPAT).
CONICET
Bvd. Brown s/nº.
(U9120ACV)Pto. Madryn
Chubut
Argentina
Tel: 542965/451301/451024/451375/45401 (Int:277)
Fax: 5429657451543

1GB gratis, Antivirus y Antispam
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On 1/27/06, gabriela escati peñaloza < [hidden email]> wrote:
> Hi, I' m having a hard time understanding the computation of degrees of freedom
So do I and I'm one of the authors of the package :)
> when runing nlme() on the following model:
>
> > formula(my data.gd)
> dLt ~ Lt  ID
>
> TasavB< function(Lt, Linf, K) (K*(LinfLt))
>
> my model.nlme < nlme (dLt ~ TasavB(Lt, Linf, K),
> data = my data.gd,
> fixed = list(Linf ~ 1, K ~ 1),
> start = list(fixed = c(70, 0.4)),
> na.action= na.include, naPattern = ~!is.na(dLt))
>
> > summary(my model.nlme)
> Nonlinear mixedeffects model fit by maximum likelihood
> Model: dLt ~ TasavB(Lt, Linf, K)
> Data: my data.gd
> AIC BIC logLik
> 13015.63 13051.57 6501.814
> Random effects:
> Formula: list(Linf ~ 1 , K ~ 1 )
> Level: ID
> Structure: General positivedefinite
> StdDev Corr
> Linf 7.3625291 Linf
> K 0.0845886 0.656
> Residual 1.6967358
> Fixed effects: list(Linf + K ~ 1)
> Value Std.Error DF tvalue pvalue
> Linf 69.32748 0.4187314 402 165.5655 <.0001
> K 0.31424 0.0047690 2549 65.8917 <.0001
> Standardized WithinGroup Residuals:
> Min Q1 Med Q3 Max
> 3.98674 0.5338083 0.02783649 0.5261591 4.750609
> Number of Observations: 2952
> Number of Groups: 403
> >
>
> Why are the DF of Linf and K different? I would apreciate if you could point me to a reference
The algorithm is described in Pinheiro and Bates (2000) "Mixedeffects
Models in S and SPLUS" published by Springer. See section 2.4.2
I would point out that there is effectively no difference between a
tdistribution with 402 df and a tdistribution with 2549 df so the
actual number of degrees of freedom is irrelevant in this case. All
you need to know is that it is "large".
I will defer to any of the "degrees of freedom police" who post to
this list to give you an explanation of why there should be different
degrees of freedom. I have been studying mixedeffects models for
nearly 15 years and I still don't understand.
> Note: I working with Splus 6.1. for Windows
Technically this email list is for questions about R. There is
another list, [hidden email], for questions about SPLUS.
>
>
> Lic. Gabriela Escati Peñaloza
> Biología y Manejo de Recursos Acuáticos
> Centro Nacional Patagónico(CENPAT).
> CONICET
> Bvd. Brown s/nº.
> (U9120ACV)Pto. Madryn
> Chubut
> Argentina
>
> Tel: 542965/451301/451024/451375/45401 (Int:277)
> Fax: 5429657451543
>
> 
> 1GB gratis, Antivirus y Antispam
> Correo Yahoo!, el mejor correo web del mundo
> Abrí tu cuenta aquí
> [[alternative HTML version deleted]]
>
>
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


Degrees of freedom for mixed models is a delicate issue  except in certain orthogonal designs.
However, I'll just point out that for lmer models, there is a simulate() function which can simulate data from a fitted model. simulate() is very fast  just like lmer(). So one way to "get around the problem" could be to evaluate the test statistic (e.g. 2 log Q) in an empirical distribution based on simulations under the model; that is to calculate a Monte Carlo pvalue. It is fairly fast to and takes about 10 lines of code to program.
Of course, Monte Carlo pvalues have their problems, but the world is not perfect....
Along similar lines, I've noticed that the anova() function for lmer models now only reports the mean squares to go into the numerator but "nothing for the denominator" of an Fstatistic; probably in recognition of the degree of freedom problem. It could be nice, however, if anova() produced even an approximate anova table which can be obtained from Wald tests. The anova function could then print that "these pvalues are large sample ones and hence only approximate"...
Best regards
Søren
________________________________
Fra: [hidden email] på vegne af Douglas Bates
Sendt: fr 27012006 17:06
Til: gabriela escati peñaloza
Cc: [hidden email]
Emne: Re: [R] how calculation degrees freedom
On 1/27/06, gabriela escati peñaloza < [hidden email]> wrote:
> Hi, I' m having a hard time understanding the computation of degrees of freedom
So do I and I'm one of the authors of the package :)
> when runing nlme() on the following model:
>
> > formula(my data.gd)
> dLt ~ Lt  ID
>
> TasavB< function(Lt, Linf, K) (K*(LinfLt))
>
> my model.nlme < nlme (dLt ~ TasavB(Lt, Linf, K),
> data = my data.gd,
> fixed = list(Linf ~ 1, K ~ 1),
> start = list(fixed = c(70, 0.4)),
> na.action= na.include, naPattern = ~!is.na(dLt))
>
> > summary(my model.nlme)
> Nonlinear mixedeffects model fit by maximum likelihood
> Model: dLt ~ TasavB(Lt, Linf, K)
> Data: my data.gd
> AIC BIC logLik
> 13015.63 13051.57 6501.814
> Random effects:
> Formula: list(Linf ~ 1 , K ~ 1 )
> Level: ID
> Structure: General positivedefinite
> StdDev Corr
> Linf 7.3625291 Linf
> K 0.0845886 0.656
> Residual 1.6967358
> Fixed effects: list(Linf + K ~ 1)
> Value Std.Error DF tvalue pvalue
> Linf 69.32748 0.4187314 402 165.5655 <.0001
> K 0.31424 0.0047690 2549 65.8917 <.0001
> Standardized WithinGroup Residuals:
> Min Q1 Med Q3 Max
> 3.98674 0.5338083 0.02783649 0.5261591 4.750609
> Number of Observations: 2952
> Number of Groups: 403
> >
>
> Why are the DF of Linf and K different? I would apreciate if you could point me to a reference
The algorithm is described in Pinheiro and Bates (2000) "Mixedeffects
Models in S and SPLUS" published by Springer. See section 2.4.2
I would point out that there is effectively no difference between a
tdistribution with 402 df and a tdistribution with 2549 df so the
actual number of degrees of freedom is irrelevant in this case. All
you need to know is that it is "large".
I will defer to any of the "degrees of freedom police" who post to
this list to give you an explanation of why there should be different
degrees of freedom. I have been studying mixedeffects models for
nearly 15 years and I still don't understand.
> Note: I working with Splus 6.1. for Windows
Technically this email list is for questions about R. There is
another list, [hidden email], for questions about SPLUS.
>
>
> Lic. Gabriela Escati Peñaloza
> Biología y Manejo de Recursos Acuáticos
> Centro Nacional Patagónico(CENPAT).
> CONICET
> Bvd. Brown s/nº.
> (U9120ACV)Pto. Madryn
> Chubut
> Argentina
>
> Tel: 542965/451301/451024/451375/45401 (Int:277)
> Fax: 5429657451543
>
> 
> 1GB gratis, Antivirus y Antispam
> Correo Yahoo!, el mejor correo web del mundo
> Abrí tu cuenta aquí
> [[alternative HTML version deleted]]
>
>
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


Søren Højsgaard < [hidden email]> writes:
> Along similar lines, I've noticed that the anova() function for lmer
> models now only reports the mean squares to go into the numerator
> but "nothing for the denominator" of an Fstatistic; probably in
> recognition of the degree of freedom problem. It could be nice,
> however, if anova() produced even an approximate anova table which
> can be obtained from Wald tests. The anova function could then print
> that "these pvalues are large sample ones and hence only
> approximate"...
I'm reasonably convinced by now that the relevant denominator is
always the residual variance, but it is happening via deep magic that
I don't quite understand... (and is quite counterintuitive to people
who are used to the traditional ANOVA decompositions in orthogonal
designs)
While we're on the subject: It would be desirable to have Wald tests
for specific terms rather than the "Type 1" (sorry, Bill) progressive
ANOVA table. Just like we already have in lme().

O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


On 1/27/06, Søren Højsgaard < [hidden email]> wrote:
> Degrees of freedom for mixed models is a delicate issue  except in certain orthogonal designs.
>
> However, I'll just point out that for lmer models, there is a simulate() function which can simulate data from a fitted model. simulate() is very fast  just like lmer(). So one way to "get around the problem" could be to evaluate the test statistic (e.g. 2 log Q) in an empirical distribution based on simulations under the model; that is to calculate a Monte Carlo pvalue. It is fairly fast to and takes about 10 lines of code to program.
>
> Of course, Monte Carlo pvalues have their problems, but the world is not perfect....
Another approach is to use mcmcsamp to derive a sample from the
posterior distribution of the parameters using Markov Chain Monte
Carlo sampling. If you are interested in intervals rather than
pvalues the HPDinterval function from the coda package can create
those.
> Along similar lines, I've noticed that the anova() function for lmer models now only reports the mean squares to go into the numerator but "nothing for the denominator" of an Fstatistic; probably in recognition of the degree of freedom problem. It could be nice, however, if anova() produced even an approximate anova table which can be obtained from Wald tests. The anova function could then print that "these pvalues are large sample ones and hence only approximate"...
I don't think the "degrees of freedom police" would find that to be a
suitable compromise. :)
>
> Fra: [hidden email] på vegne af Douglas Bates
> Sendt: fr 27012006 17:06
> Til: gabriela escati peñaloza
> Cc: [hidden email]
> Emne: Re: [R] how calculation degrees freedom
>
>
>
> On 1/27/06, gabriela escati peñaloza < [hidden email]> wrote:
> > Hi, I' m having a hard time understanding the computation of degrees of freedom
>
> So do I and I'm one of the authors of the package :)
>
> > when runing nlme() on the following model:
> >
> > > formula(my data.gd)
> > dLt ~ Lt  ID
> >
> > TasavB< function(Lt, Linf, K) (K*(LinfLt))
> >
> > my model.nlme < nlme (dLt ~ TasavB(Lt, Linf, K),
> > data = my data.gd,
> > fixed = list(Linf ~ 1, K ~ 1),
> > start = list(fixed = c(70, 0.4)),
> > na.action= na.include, naPattern = ~!is.na(dLt))
> >
> > > summary(my model.nlme)
> > Nonlinear mixedeffects model fit by maximum likelihood
> > Model: dLt ~ TasavB(Lt, Linf, K)
> > Data: my data.gd
> > AIC BIC logLik
> > 13015.63 13051.57 6501.814
> > Random effects:
> > Formula: list(Linf ~ 1 , K ~ 1 )
> > Level: ID
> > Structure: General positivedefinite
> > StdDev Corr
> > Linf 7.3625291 Linf
> > K 0.0845886 0.656
> > Residual 1.6967358
> > Fixed effects: list(Linf + K ~ 1)
> > Value Std.Error DF tvalue pvalue
> > Linf 69.32748 0.4187314 402 165.5655 <.0001
> > K 0.31424 0.0047690 2549 65.8917 <.0001
> > Standardized WithinGroup Residuals:
> > Min Q1 Med Q3 Max
> > 3.98674 0.5338083 0.02783649 0.5261591 4.750609
> > Number of Observations: 2952
> > Number of Groups: 403
> > >
> >
> > Why are the DF of Linf and K different? I would apreciate if you could point me to a reference
>
> The algorithm is described in Pinheiro and Bates (2000) "Mixedeffects
> Models in S and SPLUS" published by Springer. See section 2.4.2
>
> I would point out that there is effectively no difference between a
> tdistribution with 402 df and a tdistribution with 2549 df so the
> actual number of degrees of freedom is irrelevant in this case. All
> you need to know is that it is "large".
>
> I will defer to any of the "degrees of freedom police" who post to
> this list to give you an explanation of why there should be different
> degrees of freedom. I have been studying mixedeffects models for
> nearly 15 years and I still don't understand.
>
> > Note: I working with Splus 6.1. for Windows
>
> Technically this email list is for questions about R. There is
> another list, [hidden email], for questions about SPLUS.
>
> >
> >
> > Lic. Gabriela Escati Peñaloza
> > Biología y Manejo de Recursos Acuáticos
> > Centro Nacional Patagónico(CENPAT).
> > CONICET
> > Bvd. Brown s/nº.
> > (U9120ACV)Pto. Madryn
> > Chubut
> > Argentina
> >
> > Tel: 542965/451301/451024/451375/45401 (Int:277)
> > Fax: 5429657451543
> >
> > 
> > 1GB gratis, Antivirus y Antispam
> > Correo Yahoo!, el mejor correo web del mundo
> > Abrí tu cuenta aquí
> > [[alternative HTML version deleted]]
> >
> >
> >
> > ______________________________________________
> > [hidden email] mailing list
> > https://stat.ethz.ch/mailman/listinfo/rhelp> > PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html> >
> >
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide! http://www.Rproject.org/postingguide.html>
>
>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


On 27 Jan 2006 23:08:28 +0100, Peter Dalgaard < [hidden email]> wrote:
> Søren Højsgaard < [hidden email]> writes:
>
> > Along similar lines, I've noticed that the anova() function for lmer
> > models now only reports the mean squares to go into the numerator
> > but "nothing for the denominator" of an Fstatistic; probably in
> > recognition of the degree of freedom problem. It could be nice,
> > however, if anova() produced even an approximate anova table which
> > can be obtained from Wald tests. The anova function could then print
> > that "these pvalues are large sample ones and hence only
> > approximate"...
>
> I'm reasonably convinced by now that the relevant denominator is
> always the residual variance, but it is happening via deep magic that
> I don't quite understand... (and is quite counterintuitive to people
> who are used to the traditional ANOVA decompositions in orthogonal
> designs)
Not deep magic for you, Peter. The slot called rXy in the fitted
model is analogous to the first p components of the "effects"
component in an lm model. Cut it up according to the terms and sum
the squares.
> While we're on the subject: It would be desirable to have Wald tests
> for specific terms rather than the "Type 1" (sorry, Bill) progressive
> ANOVA table. Just like we already have in lme().
I think this is the point where I mention the Open Source nature of
project. Sorry to say that it is not a priority for me right now.
______________________________________________
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https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


Douglas Bates < [hidden email]> writes:
> > Of course, Monte Carlo pvalues have their problems, but the world
> > is not perfect....
>
> Another approach is to use mcmcsamp to derive a sample from the
> posterior distribution of the parameters using Markov Chain Monte
> Carlo sampling. If you are interested in intervals rather than
> pvalues the HPDinterval function from the coda package can create
> those.
>
We (Søren and I) actually had a look at that, and it seems not to
solve the problem. Rather, mcmcsamp tends to reproduce the Wald style
inference (infinite DF) if you use a suitably vague prior.
It's a bit hard to understand clearly, but I think the crux is that
any Bayes inference only depends on data through the likelihood
function. The distribution of the likelihood never enters (the
hardcore Bayesian of course won't care). However, the nature of DF
corrections is that the LRT does not have its asymptotic distribution,
and mcmc has no way of picking that up.

O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


Douglas Bates < [hidden email]> writes:
> > I'm reasonably convinced by now that the relevant denominator is
> > always the residual variance, but it is happening via deep magic that
> > I don't quite understand... (and is quite counterintuitive to people
> > who are used to the traditional ANOVA decompositions in orthogonal
> > designs)
>
> Not deep magic for you, Peter. The slot called rXy in the fitted
> model is analogous to the first p components of the "effects"
> component in an lm model. Cut it up according to the terms and sum
> the squares.
Yes, that's the bit I do understand. The magic bit is that at that
point, you have removed the effect of the Z, in a penalized fashion,
and somehow this manages to give the right thing.
Consider a balanced design with a number of observations for each
subject, and subjects divided into groups, subject effects considered
random. In traditional aov, you end up dividing the "group" SS by the
"subject" SS. However in lmer, you remove the subject effect (in a
BLUP sort of way) from everything and this gets the "group" SS
adjusted so that it matches the residual SS instead. I'm quite
prepared to believe it; I just don't think it is trivial.

O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


In connection with calculating Monte Carlo pvalues based on sampled data sets: The calculations involve something like
update(lmer.model, data=newdata)
where newdata is a simulated dataset comming from simulate(lmer.model). I guess the update could be faster if one could supply the update function with the parameter estimates from the original fit of the lmer.model as starting values. Is this possible to achieve??
Best
Søren
________________________________
Fra: [hidden email] på vegne af Peter Dalgaard
Sendt: lø 28012006 01:12
Til: Douglas Bates
Cc: Søren Højsgaard; [hidden email]
Emne: Re: [R] how calculation degrees freedom
Douglas Bates < [hidden email]> writes:
> > Of course, Monte Carlo pvalues have their problems, but the world
> > is not perfect....
>
> Another approach is to use mcmcsamp to derive a sample from the
> posterior distribution of the parameters using Markov Chain Monte
> Carlo sampling. If you are interested in intervals rather than
> pvalues the HPDinterval function from the coda package can create
> those.
>
We (Søren and I) actually had a look at that, and it seems not to
solve the problem. Rather, mcmcsamp tends to reproduce the Wald style
inference (infinite DF) if you use a suitably vague prior.
It's a bit hard to understand clearly, but I think the crux is that
any Bayes inference only depends on data through the likelihood
function. The distribution of the likelihood never enters (the
hardcore Bayesian of course won't care). However, the nature of DF
corrections is that the LRT does not have its asymptotic distribution,
and mcmc has no way of picking that up.

O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide! http://www.Rproject.org/postingguide.html


Dear Dr. Bates,
Thank you very much for your response.
 Douglas Bates < [hidden email]> escribió:
> On 1/27/06, gabriela escati peñaloza
> < [hidden email]> wrote:
> > Hi, I' m having a hard time understanding the
> computation of degrees of freedom
>
> So do I and I'm one of the authors of the package
> :)
>
> > when runing nlme() on the following model:
> >
> > > formula(my data.gd)
> > dLt ~ Lt  ID
> >
> > TasavB< function(Lt, Linf, K) (K*(LinfLt))
> >
> > my model.nlme < nlme (dLt ~ TasavB(Lt, Linf,
> K),
> > data = my data.gd,
> > fixed = list(Linf ~ 1, K ~ 1),
> > start = list(fixed = c(70, 0.4)),
> > na.action= na.include, naPattern = ~!is.na(dLt))
> >
> > > summary(my model.nlme)
> > Nonlinear mixedeffects model fit by maximum
> likelihood
> > Model: dLt ~ TasavB(Lt, Linf, K)
> > Data: my data.gd
> > AIC BIC logLik
> > 13015.63 13051.57 6501.814
> > Random effects:
> > Formula: list(Linf ~ 1 , K ~ 1 )
> > Level: ID
> > Structure: General positivedefinite
> > StdDev Corr
> > Linf 7.3625291 Linf
> > K 0.0845886 0.656
> > Residual 1.6967358
> > Fixed effects: list(Linf + K ~ 1)
> > Value Std.Error DF tvalue pvalue
> > Linf 69.32748 0.4187314 402 165.5655 <.0001
> > K 0.31424 0.0047690 2549 65.8917 <.0001
> > Standardized WithinGroup Residuals:
> > Min Q1 Med Q3
> Max
> > 3.98674 0.5338083 0.02783649 0.5261591
> 4.750609
> > Number of Observations: 2952
> > Number of Groups: 403
> > >
> >
> > Why are the DF of Linf and K different? I would
> apreciate if you could point me to a reference
>
> The algorithm is described in Pinheiro and Bates
> (2000) "Mixedeffects
> Models in S and SPLUS" published by Springer. See
> section 2.4.2
I had consulted
the algorithm described in Pinheiro and Bates.
>
> I would point out that there is effectively no
> difference between a
> tdistribution with 402 df and a tdistribution with
> 2549 df so the
> actual number of degrees of freedom is irrelevant in
> this case. All
> you need to know is that it is "large".
However, what I don't understand (among other things)
is why my two parameters appear to be estimated at
different grouping levels (based on the DF values).
Affect this different values of DF at the estimates
parameters? The estimates fixed effects were get at
the same level of grouping?
>
> I will defer to any of the "degrees of freedom
> police" who post to
> this list to give you an explanation of why there
> should be different
> degrees of freedom. I have been studying
> mixedeffects models for
> nearly 15 years and I still don't understand.
>
> > Note: I working with Splus 6.1. for Windows
>
> Technically this email list is for questions about
> R. There is
> another list, [hidden email], for
> questions about SPLUS.
> I apreciate any response.
Lic. Gabriela Escati Peñaloza
Biología y Manejo de Recursos Acuáticos
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On 1/29/06, Søren Højsgaard < [hidden email]> wrote:
> In connection with calculating Monte Carlo pvalues based on sampled data sets: The calculations involve something like
> update(lmer.model, data=newdata)
> where newdata is a simulated dataset comming from simulate(lmer.model). I guess the update could be faster if one could supply the update function with the parameter estimates from the original fit of the lmer.model as starting values. Is this possible to achieve??
Possible  yes. (See the quote in the fortunes package about "This is
R. There is no if  only how.")
Roughly what one does is
 Take a copy of the fitted model object as, say, "mer"
 .Call("mer_update_y", mer, ynew, PACKAGE = "Matrix")
 arrange for a suitable call to
LMEoptimize(mer) < controloptions
The last part is a little tricky in that "LMEoptimize<" is hidden in
the Matrix namespace.
I'm happy to write a function to do this but my creativity is at a low
ebb and I would appreciate any suggestions for a suitable name and
calling sequence. Even more welcome would be an existing generic
function for which something like this could be a method.
> Best
> Søren
>
> ________________________________
>
> Fra: [hidden email] på vegne af Peter Dalgaard
> Sendt: lø 28012006 01:12
> Til: Douglas Bates
> Cc: Søren Højsgaard; [hidden email]
> Emne: Re: [R] how calculation degrees freedom
>
>
>
> Douglas Bates < [hidden email]> writes:
>
>
> > > Of course, Monte Carlo pvalues have their problems, but the world
> > > is not perfect....
> >
> > Another approach is to use mcmcsamp to derive a sample from the
> > posterior distribution of the parameters using Markov Chain Monte
> > Carlo sampling. If you are interested in intervals rather than
> > pvalues the HPDinterval function from the coda package can create
> > those.
> >
>
> We (Søren and I) actually had a look at that, and it seems not to
> solve the problem. Rather, mcmcsamp tends to reproduce the Wald style
> inference (infinite DF) if you use a suitably vague prior.
>
> It's a bit hard to understand clearly, but I think the crux is that
> any Bayes inference only depends on data through the likelihood
> function. The distribution of the likelihood never enters (the
> hardcore Bayesian of course won't care). However, the nature of DF
> corrections is that the LRT does not have its asymptotic distribution,
> and mcmc has no way of picking that up.
>
>
> 
> O__  Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_  Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*)  University of Copenhagen Denmark Ph: (+45) 35327918
> ~~~~~~~~~~  ( [hidden email]) FAX: (+45) 35327907
>
>
>
>
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