[R-sig-ME] Difference lme4 and nlme
Daniel
dmsilv at gmail.com
Wed Feb 23 21:01:55 CET 2011
Yes,
I'm try to fit a model of candidates campaign revenues using 3 levels model.
My theoretical assumption is that parties is nested within districts,
thus my model look likes:
REVENUES ~ Incumbency + Gender + GOV + IPC + Partisan +
(1|DISTRICT:PARTY), data=data, REML = TRUE)
where,
REVENUES = total money received
Level 1
Incumbency = dummy
Gender = dummy
Level 2
GOV = dummy if party runs state government
IPC = Ordinal variable to Intra-party competition
Level 3
Partisan = total of partisan in the state
However, this discussion drove me to a importante question: am I
account for possible main effect of PARTY or DISTRICT? If these points
is plausible my model should be something like this:
REVENUES ~ Incumbency + Gender + GOV + IPC + Partisan + (1|PARTY) +
(1|DISTRICT) + (1|DISTRICT:PARTY), data=data, REML = TRUE)
What you think?
Best,
Daniel
On Wed, Feb 23, 2011 at 4:04 PM, Douglas Bates <bates at stat.wisc.edu> wrote:
> On Wed, Feb 23, 2011 at 12:19 PM, Daniel <dmsilv at gmail.com> wrote:
>> This sound a controversial issue. If I change "(1|J) + (1|J:PARTY)"
>> for "(1|PARTY) + (1|J:PARTY)" I get great different outcomes. So,
>> first I need to place third level (J) and second PARTY nested within
>> J, right?
>>
>> So, I take this opportunity to inform that scripts of "Linear Mixed
>> Models: A Practical Guide Using Statistical Software" by Brady et al;
>> perhaps are wrong. Scripts can be found at
>> (http://www-personal.umich.edu/~bwest/chapter4.html)
>
> As Andrzej is one of the authors of that book I'll let him respond
> about the scripts.
>
> Can you give us some background to the study - in particular, what
> does J represent and what does PARTY represent?
>
> This sort of confusion is, in my opinion, unnecessary. If the factors
> are defined sensibly - avoiding what I call "implicit nesting" - then
> the model specification is straightforward.
>
>>
>> Thanks,
>> Daniel
>>
>> On Wed, Feb 23, 2011 at 12:55 PM, Andrzej Galecki <agalecki at umich.edu> wrote:
>>>
>>> Hello to everyone,
>>>
>>> Actually, the same type of mistake occurred in an earlier email.
>>>
>>>
>>> "If you do indeed want to have PARTY nested within J then your call to
>>> lmer should use the formula
>>>
>>> REVENUES ~ INCUMBENCY + (1|PARTY) + (1|J:PARTY)"
>>>
>>>
>>> Preferred notation was used incorrectly. It should be:
>>>
>>> REVENUES ~ INCUMBENCY + (1|J) + (1|J:PARTY)
>>>
>>>
>>> Thank you
>>>
>>> Andrzej Galecki
>>> University of Michigan
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 2/23/2011 10:39 AM, Douglas Bates wrote:
>>>>
>>>> On Wed, Feb 23, 2011 at 8:39 AM, ONKELINX, Thierry
>>>> <Thierry.ONKELINX at inbo.be> wrote:
>>>>>
>>>>> "(1|PARTY) + (1|J:PARTY)" and "(1|J/PARTY)" are equal
>>>>
>>>> Actually (1|PARTY) + (1|J:PARTY) is equal to (1|PARTY/J). It is easy
>>>> to confuse these which is why I prefer not to use the (1|F/G)
>>>> notation.
>>>>
>>>>> "(1|PARTY) + (1|J:PARTY)" and "(J|PARTY)" are not equal
>>>>>
>>>>> ----------------------------------------------------------------------------
>>>>> ir. Thierry Onkelinx
>>>>> Instituut voor natuur- en bosonderzoek
>>>>> team Biometrie& Kwaliteitszorg
>>>>> Gaverstraat 4
>>>>> 9500 Geraardsbergen
>>>>> Belgium
>>>>>
>>>>> Research Institute for Nature and Forest
>>>>> team Biometrics& Quality Assurance
>>>>> Gaverstraat 4
>>>>> 9500 Geraardsbergen
>>>>> Belgium
>>>>>
>>>>> tel. + 32 54/436 185
>>>>> Thierry.Onkelinx at inbo.be
>>>>> www.inbo.be
>>>>>
>>>>> To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
>>>>> ~ Sir Ronald Aylmer Fisher
>>>>>
>>>>> The plural of anecdote is not data.
>>>>> ~ Roger Brinner
>>>>>
>>>>> The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
>>>>> ~ John Tukey
>>>>>
>>>>>
>>>>>> -----Oorspronkelijk bericht-----
>>>>>> Van: r-sig-mixed-models-bounces at r-project.org
>>>>>> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Iker
>>>>>> Vaquero Alba
>>>>>> Verzonden: woensdag 23 februari 2011 15:30
>>>>>> Aan: Daniel; Douglas Bates
>>>>>> CC: R-sig-mixed-models at r-project.org
>>>>>> Onderwerp: Re: [R-sig-ME] Difference lme4 and nlme
>>>>>>
>>>>>>
>>>>>>
>>>>>> Just a technical question: Would "(1|PARTY) + (1|J:PARTY)"
>>>>>> be equal to "(1|J/PARTY)" and this to "(J|PARTY)"?
>>>>>>
>>>>>> I've tried the last two ones and as long as I saw, I got
>>>>>> the same results, but I might have overlooked something.
>>>>>>
>>>>>> Thank you. Regards.
>>>>>>
>>>>>> Iker
>>>>>>
>>>>>> --- El mié, 23/2/11, Douglas Bates<bates at stat.wisc.edu> escribió:
>>>>>>
>>>>>> De: Douglas Bates<bates at stat.wisc.edu>
>>>>>> Asunto: Re: [R-sig-ME] Difference lme4 and nlme
>>>>>> Para: "Daniel"<dmsilv at gmail.com>
>>>>>> CC: R-sig-mixed-models at r-project.org
>>>>>> Fecha: miércoles, 23 de febrero, 2011 15:08
>>>>>>
>>>>>> Notice that the first model has 27 levels for J and the
>>>>>> second model has 465 levels for PARTY %in% J. That's the difference.
>>>>>>
>>>>>> If you do indeed want to have PARTY nested within J then your
>>>>>> call to lmer should use the formula
>>>>>>
>>>>>> REVENUES ~ INCUMBENCY + (1|PARTY) + (1|J:PARTY)
>>>>>>
>>>>>>
>>>>>> On Wed, Feb 23, 2011 at 6:27 AM, Daniel<dmsilv at gmail.com> wrote:
>>>>>>>
>>>>>>> Hello list,
>>>>>>>
>>>>>>> I'm just try to find out how can I produce the results
>>>>>>
>>>>>> using both packages.
>>>>>>>
>>>>>>> Perhaps I'm using different equation. Trailer model are
>>>>>>
>>>>>> consistent to
>>>>>>>
>>>>>>> Stata output using (tmixed REVENUES INCUMBENCY || J: || PARTY:)
>>>>>>>
>>>>>>> lme2<-
>>>>>>
>>>>>> lmer(REVENUES~INCUMBENCY+(1|J)+(1|PARTY),data=data,na.action =
>>>>>>>
>>>>>>> "na.omit", REML=TRUE)
>>>>>>>
>>>>>>> Linear mixed model fit by REML
>>>>>>> Formula: REVENUES ~ INCUMBENCY + (1 | J) + (1 | PARTY)
>>>>>>> Data: data
>>>>>>> AIC BIC logLik deviance REMLdev
>>>>>>> 78123 78153 -39057 78154 78113
>>>>>>> Random effects:
>>>>>>> Groups Name Variance Std.Dev.
>>>>>>> J (Intercept) 9.6263e+08 31026 PARTY (Intercept)
>>>>>>
>>>>>> 1.7502e+09
>>>>>>>
>>>>>>> 41836 Residual 3.0534e+10 174741 Number of obs: 2894,
>>>>>>> groups: J, 27; PARTY, 27
>>>>>>>
>>>>>>> Fixed effects:
>>>>>>> Estimate Std. Error t value
>>>>>>> (Intercept) 34244 11657 2.938 INCUMBENCY 211495
>>>>>>> 9536 22.178
>>>>>>>
>>>>>>> Correlation of Fixed Effects:
>>>>>>> (Intr)
>>>>>>> INCUMBENCY -0.097
>>>>>>>
>>>>>>> lme3<- lme(REVENUES~INCUMBENCY, random=~1
>>>>>>> |J/PARTY,data=data,na.action = "na.omit", REML=TRUE)
>>>>>>>
>>>>>>> Linear mixed-effects model fit by REML
>>>>>>> Data: data
>>>>>>> Log-restricted-likelihood: -39078.07
>>>>>>> Fixed: REVENUES ~ INCUMBENCY
>>>>>>> (Intercept) INCUMBENCY
>>>>>>> 52469.19 220521.74
>>>>>>>
>>>>>>> Random effects:
>>>>>>> Formula: ~1 | J
>>>>>>> (Intercept)
>>>>>>> StdDev: 25424.31
>>>>>>>
>>>>>>> Formula: ~1 | PARTY %in% J
>>>>>>> (Intercept) Residual
>>>>>>> StdDev: 45574.5 173465.7
>>>>>>>
>>>>>>> Number of Observations: 2894
>>>>>>> Number of Groups:
>>>>>>> J PARTY %in% J
>>>>>>> 27 465
>>>>>>>
>>>>>>> --
>>>>>>> Daniel Marcelino
>>>>>>> Skype: dmsilv
>>>>>>> http://sites.google.com/
>>>>>>>
>>>>>>> [[alternative HTML version deleted]]
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>>
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>>>>>>
>>>>>>
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>>>>>>
>>>>>>
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>>
>>
>>
>> --
>> Daniel Marcelino
>> Skype: dmsilv
>> http://sites.google.com/
>>
>
--
Daniel Marcelino
Skype: dmsilv
http://sites.google.com/
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