[R-sig-ME] R-sig-mixed-models Digest, Vol 27, Issue 36
Jarrod Hadfield
j.hadfield at ed.ac.uk
Thu Apr 2 12:36:54 CEST 2009
Hi Luciano,
Perhaps you could tabulate your results so we could see whether this
really is an over-fitting problem or a numerical problem. Something
like:
table(lHatching, HatchOrder, Year)
should do it.
Cheers,
Jarrod
On 1 Apr 2009, at 22:04, Luciano La Sala wrote:
>
> Dear Ken and Jarrod,
>
> Thank you very much for shedding some light on my problem!
> Besides the points you've made, I was told that I could avoid over-
> fitting in my second model by treating the interaction term as two
> continuous variables instead of categorical ones
> (HatchingOrder*Year). Well, after doind so, the output says "Error
> in asMethod(object) : matrix is not symmetric [1,2]", so I guess
> that that is not a feasible solution to the problem either, and I
> wonder how far should one go to find one?
>
> If my model fell prey to Hauck-Donner effect - which may be causing
> the std.. errors to be overestimated and the signifficance of the
> effect to be missed - maybe I just should stop turturing my data and
> admit that it is sparse?
>
> Should you come up with any ideas, I'd be glad to hear them.
>
> Luciano
>
>
>
>
> --- El lun 30-mar-09, r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org
> > escribió:
>
>> De: r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org
>> >
>> Asunto: R-sig-mixed-models Digest, Vol 27, Issue 36
>> Para: r-sig-mixed-models at r-project.org
>> Fecha: lunes, 30 de marzo de 2009, 6:27 pm
>> Send R-sig-mixed-models mailing list submissions to
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>>
>>
>> Today's Topics:
>>
>> 1. Re: Can interaction term cause Estimates and Std.
>> Errors to
>> be too large? (Ken Beath)
>> 2. Re: Can interaction term cause Estimates and Std.
>> Errors to
>> be too large? (Jarrod Hadfield)
>> 3. Re: Can interaction term cause Estimates and Std.
>> Errors to
>> be too large? (Ken Beath)
>> 4. Meta-analysis using lmer (Yu-Kang Tu)
>> 5. Mixed Model for Travel Distance (Chuck Cleland)
>> 6. Re: Mixed Model for Travel Distance (Dimitris
>> Rizopoulos)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Mon, 30 Mar 2009 21:08:58 +1100
>> From: Ken Beath <ken at kjbeath.com.au>
>> Subject: Re: [R-sig-ME] Can interaction term cause
>> Estimates and Std.
>> Errors to be too large?
>> To: Jarrod Hadfield <j.hadfield at ed.ac.uk>
>> Cc: r-sig-mixed-models at r-project.org,
>> lucianolasala at yahoo.com.ar
>> Message-ID:
>> <68347D8C-AE1C-48BF-B194-D7E1DDD50B50 at kjbeath.com.au>
>> Content-Type: text/plain; charset=US-ASCII; format=flowed;
>> delsp=yes
>>
>> I meant overfitting in the sense of trying to fit too
>> complex a model,
>> which is the same as what you are describing. Gelman has
>> some papers
>> on the use of priors, one is
>> http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1231424214
>>
>> In the case of complete separation the results seem to be
>> very
>> dependent on the prior which doesn't look to be a good
>> thing. It would
>> appear much better to admit that there is insufficient data
>> to perform
>> the analysis.
>>
>> Ken
>>
>>
>> On 30/03/2009, at 7:48 PM, Jarrod Hadfield wrote:
>>
>>> Hi,
>>>
>>> I think it unlikely that the problem arises through
>> overfitting in
>>> the sense that there are too many parameters for the
>> amount of
>>> data. It's more likely that the underlying
>> probabilities really are
>>> extreme for some categories causing what are also
>> known as "extreme
>>> category problems" (eg Miztal 1998 J. Dairy
>> Science 72 1557-1568):
>>> the binary variable in one or more groups is always 0
>> or 1, even
>>> though there are probably many eggs in most
>> categories. A solution
>>> to this type of problem is to place an informative
>> prior on the
>>> fixed effects to stop them wandering into extreme
>> values on the
>>> logit scale. For the purist this may be anathema, but
>> as a practical
>>> solution it seems to work quite well. Having a normal
>> prior on the
>>> logit scale with mean zero and variance pi, is the
>> closest (I
>>> think?) to a uniform prior on the probability scale.
>> If there are
>>> more elegant solutions to the problem I'd be
>> interested to hear
>>> about them.
>>>
>>> Cheers,
>>>
>>> Jarrod
>>>
>>>
>>>
>>> --
>>> The University of Edinburgh is a charitable body,
>> registered in
>>> Scotland, with registration number SC005336.
>>>
>>>
>>
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Mon, 30 Mar 2009 11:21:39 +0100
>> From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
>> Subject: Re: [R-sig-ME] Can interaction term cause
>> Estimates and Std.
>> Errors to be too large?
>> To: Ken Beath <ken at kjbeath.com.au>
>> Cc: r-sig-mixed-models at r-project.org,
>> lucianolasala at yahoo.com.ar
>> Message-ID:
>> <AC5DBE6D-DEEF-47A2-90BE-071FE9CBCC79 at ed.ac.uk>
>> Content-Type: text/plain; charset="us-ascii";
>> Format="flowed";
>> DelSp="yes"
>>
>> Hi Ken,
>>
>> Thanks for the reference, it looks interesting. I disagree
>> that
>> Luciano's second model should be classified as over
>> fitting. Imagine
>> this....
>>
>> y<-rbinom(100, 1, c(0.001, 0.999))
>> x<-gl(2,1,100)
>>
>> summary(glm(y~1, family="binomial"))
>> summary(glm(y~x, family="binomial"))
>>
>> There is a very high probability of complete separation,
>> the second
>> model gives non-significant p-values for the effect of x,
>> but I think
>> it would be a mistake to say the 2nd model is over-fitted
>> and should
>> be avoided.
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>> On 30 Mar 2009, at 11:08, Ken Beath wrote:
>>
>>> I meant overfitting in the sense of trying to fit too
>> complex a
>>> model, which is the same as what you are describing.
>> Gelman has some
>>> papers on the use of priors, one is
>> http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1231424214
>>
>>> In the case of complete separation the results seem
>> to be very
>>> dependent on the prior which doesn't look to be a
>> good thing. It
>>> would appear much better to admit that there is
>> insufficient data to
>>> perform the analysis.
>>>
>>> Ken
>>>
>>>
>>> On 30/03/2009, at 7:48 PM, Jarrod Hadfield wrote:
>>>
>>>> Hi,
>>>>
>>>> I think it unlikely that the problem arises
>> through overfitting in
>>>> the sense that there are too many parameters for
>> the amount of
>>>> data. It's more likely that the underlying
>> probabilities really
>>>> are extreme for some categories causing what are
>> also known as
>>>> "extreme category problems" (eg Miztal
>> 1998 J. Dairy Science 72
>>>> 1557-1568): the binary variable in one or more
>> groups is always 0
>>>> or 1, even though there are probably many eggs in
>> most
>>>> categories. A solution to this type of problem is
>> to place an
>>>> informative prior on the fixed effects to stop
>> them wandering into
>>>> extreme values on the logit scale. For the purist
>> this may be
>>>> anathema, but as a practical solution it seems to
>> work quite well.
>>>> Having a normal prior on the logit scale with mean
>> zero and
>>>> variance pi, is the closest (I think?) to a
>> uniform prior on the
>>>> probability scale. If there are more elegant
>> solutions to the
>>>> problem I'd be interested to hear about them.
>>>>
>>>> Cheers,
>>>>
>>>> Jarrod
>>>>
>>>>
>>>>
>>>> --
>>>> The University of Edinburgh is a charitable body,
>> registered in
>>>> Scotland, with registration number SC005336.
>>>>
>>>>
>>>
>>>
>>
>> -------------- next part --------------
>> An embedded and charset-unspecified text was scrubbed...
>> Name: not available
>> URL:
>> <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20090330/df46ef59/attachment-0001.pl
>> >
>>
>> ------------------------------
>>
>> Message: 3
>> Date: Mon, 30 Mar 2009 21:50:53 +1100
>> From: Ken Beath <ken at kjbeath.com.au>
>> Subject: Re: [R-sig-ME] Can interaction term cause
>> Estimates and Std.
>> Errors to be too large?
>> To: Jarrod Hadfield <j.hadfield at ed.ac.uk>
>> Cc: "r-sig-mixed-models at r-project.org Models"
>> <r-sig-mixed-models at r-project.org>,
>> lucianolasala at yahoo.com.ar
>> Message-ID:
>> <AF2C8F6D-8857-4855-BAED-8DD9B525212D at kjbeath.com.au>
>> Content-Type: text/plain; charset=US-ASCII; format=flowed;
>> delsp=yes
>>
>> On 30/03/2009, at 9:21 PM, Jarrod Hadfield wrote:
>>
>>> Hi Ken,
>>>
>>> Thanks for the reference, it looks interesting. I
>> disagree that
>>> Luciano's second model should be classified as
>> over fitting. Imagine
>>> this....
>>>
>>> y<-rbinom(100, 1, c(0.001, 0.999))
>>> x<-gl(2,1,100)
>>>
>>> summary(glm(y~1, family="binomial"))
>>> summary(glm(y~x, family="binomial"))
>>>
>>> There is a very high probability of complete
>> separation, the second
>>> model gives non-significant p-values for the effect of
>> x, but I
>>> think it would be a mistake to say the 2nd model is
>> over-fitted and
>>> should be avoided.
>>>
>>> Cheers,
>>>
>>> Jarrod
>>>
>>
>> My original posting said "usually" and obviously
>> you can create data
>> with perfect or almost perfect correlation over a large
>> table, but in
>> practice it commonly happens because there is a small
>> table. One good
>> reason for producing some descriptive tables before
>> fitting.
>>
>> Ken
>>
>>
>>>
>>> On 30 Mar 2009, at 11:08, Ken Beath wrote:
>>>
>>>> I meant overfitting in the sense of trying to fit
>> too complex a
>>>> model, which is the same as what you are
>> describing. Gelman has
>>>> some papers on the use of priors, one is
>> http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1231424214
>>
>>>> In the case of complete separation the results
>> seem to be very
>>>> dependent on the prior which doesn't look to
>> be a good thing. It
>>>> would appear much better to admit that there is
>> insufficient data
>>>> to perform the analysis.
>>>>
>>>> Ken
>>>>
>>>>
>>>> On 30/03/2009, at 7:48 PM, Jarrod Hadfield wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I think it unlikely that the problem arises
>> through overfitting in
>>>>> the sense that there are too many parameters
>> for the amount of
>>>>> data. It's more likely that the
>> underlying probabilities really
>>>>> are extreme for some categories causing what
>> are also known as
>>>>> "extreme category problems" (eg
>> Miztal 1998 J. Dairy Science 72
>>>>> 1557-1568): the binary variable in one or more
>> groups is always 0
>>>>> or 1, even though there are probably many eggs
>> in most
>>>>> categories. A solution to this type of
>> problem is to place an
>>>>> informative prior on the fixed effects to stop
>> them wandering into
>>>>> extreme values on the logit scale. For the
>> purist this may be
>>>>> anathema, but as a practical solution it seems
>> to work quite
>>>>> well. Having a normal prior on the logit
>> scale with mean zero and
>>>>> variance pi, is the closest (I think?) to a
>> uniform prior on the
>>>>> probability scale. If there are more elegant
>> solutions to the
>>>>> problem I'd be interested to hear about
>> them.
>>>>>
>>>>> Cheers,
>>>>>
>>>>> Jarrod
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> The University of Edinburgh is a charitable
>> body, registered in
>>>>> Scotland, with registration number SC005336.
>>>>>
>>>>>
>>>>
>>>>
>>>
>>> The University of Edinburgh is a charitable body,
>> registered in
>>> Scotland, with registration number SC005336.
>>
>>
>>
>> ------------------------------
>>
>> Message: 4
>> Date: Mon, 30 Mar 2009 18:16:46 +0100
>> From: Yu-Kang Tu <Y.K.Tu at leeds.ac.uk>
>> Subject: [R-sig-ME] Meta-analysis using lmer
>> To: "'r-sig-mixed-models at r-project.org'"
>> <r-sig-mixed-models at r-project.org>
>> Message-ID:
>> <7131EF1EF27893479833FCF59E7AAC440B3E818BE1 at HERMES9.ds.leeds.ac.uk>
>> Content-Type: text/plain; charset="us-ascii"
>>
>>
>> Hi,
>>
>> I am trying to use lme and lmer to do random effects
>> meta-analysis as described in Hox (2002) and UCLA website:
>> http://www.ats.ucla.edu/stat/mlwin/examples/ma_hox/chapter8.htm
>>
>> Basically, what I want to do is to constraint one residual
>> error variance to be unity and use the inverse of standard
>> errors as the covariate (weight) for this variance. And an
>> additional random effects terms is used to estimate the
>> between-study variation. I did take a look at the Pinheiro
>> & Bates book on varFunc, but unfortunately, I cannot
>> figure out how this can be done. Any suggestions/advices
>> will be greatly appreciated. Many thanks.
>>
>> Yu-Kang
>> --------------------------------------------
>> Dr Yu-Kang Tu
>> Senior Clinical Research Fellow
>> Division of Biostatistics, Centre for Epidemiology and
>> Biostatistics
>> Leeds Institute of Genetics, Health and Therapeutics, and
>> Department of Periodontology, Leeds Dental Institute
>> Room 8.01, Level 8, Worsley Building,
>> Clarendon Way
>> University of Leeds, LS2 9JT
>> Email: y.k.tu at leeds.ac.uk
>> Tel: +44 (0) 113 3431877
>> Fax: +44 (0) 113 3434877
>>
>>
>>
>> ------------------------------
>>
>> Message: 5
>> Date: Mon, 30 Mar 2009 13:29:15 -0400
>> From: Chuck Cleland <ccleland at optonline.net>
>> Subject: [R-sig-ME] Mixed Model for Travel Distance
>> To: r-sig-mixed-models at r-project.org
>> Message-ID: <49D1016B.8050808 at optonline.net>
>> Content-Type: text/plain; charset=ISO-8859-1
>>
>> Hello:
>> I am attempting to model the distance that clients travel
>> to a
>> treatment program. There are 14385 clients nested in 83
>> treatment
>> programs (the grouping factor). The raw data are in miles
>> driven from
>> the client's residence to the treatment program. A
>> natural logarithm
>> transformation of miles driven works well to reduce the
>> positive skew in
>> miles driven. I fit a model with lme() that looks like
>> this:
>>
>> Linear mixed-effects model fit by REML
>> Data: dist.df
>> AIC BIC logLik
>> 38145.37 38319.54 -19049.68
>>
>> Random effects:
>> Formula: ~1 | PROGRAM
>> (Intercept) Residual
>> StdDev: 0.4268969 0.8988483
>>
>> Fixed effects: log(DIST5DZ + 1) ~ QUAD + BEAL_TRI +
>> log(RZIPAREA + 1) +
>> log(PZIPAREA + 1) + AGE.TRI + P3GEND + P5RACEX + EMPLD +
>> P13REASN +
>> METHFST + URGE.DI + WDRAW.DI + RX_30 + P7HR30
>>
>> Value Std.Error DF
>> t-value p-value
>> (Intercept) 1.2235603 0.13153231 14288
>> 9.30236 0.0000
>> QUADSouthEast 0.2100666 0.13200891 76
>> 1.59131 0.1157
>> QUADMidWest 0.2760390 0.15709516 76
>> 1.75715 0.0829
>> QUADWest -0.1655914 0.15536003 76
>> -1.06586 0.2899
>> BEAL_TRI250K-1M -0.0264939 0.11724713 76
>> -0.22597 0.8218
>> BEAL_TRI<250K -0.0965256 0.16399464 76
>> -0.58859 0.5579
>> log(RZIPAREA + 1) 0.2965304 0.00757138 14288
>> 39.16463 0.0000
>> log(PZIPAREA + 1) -0.0042061 0.04413826 76
>> -0.09529 0.9243
>> AGE.TRI30-43 -0.0309444 0.01789442 14288
>> -1.72927 0.0838
>> AGE.TRI43-83 -0.1281177 0.02168648 14288
>> -5.90772 0.0000
>> P3GENDFemale -0.0195289 0.01632703 14288
>> -1.19611 0.2317
>> P5RACEXLatino -0.3527584 0.02904416 14288
>> -12.14559 0.0000
>> P5RACEXBlack -0.5485861 0.03306146 14288
>> -16.59292 0.0000
>> P5RACEXOther -0.1580669 0.04811350 14288
>> -3.28529 0.0010
>> EMPLDYes 0.0098856 0.01650635 14288
>> 0.59890 0.5493
>> P13REASNYes -0.0095057 0.01650128 14288
>> -0.57606 0.5646
>> METHFSTYes 0.0073478 0.01672948 14288
>> 0.43921 0.6605
>> URGE.DI Strong-VeryStrong -0.0272769 0.02400068 14288
>> -1.13651 0.2558
>> WDRAW.DISevere-VerySevere 0.0012054 0.01810067 14288
>> 0.06659 0.9469
>> RX_30Yes 0.0934411 0.02165389 14288
>> 4.31521 0.0000
>> P7HR30Yes -0.0408189 0.02256842 14288
>> -1.80867 0.0705
>>
>> Standardized Within-Group Residuals:
>> Min Q1 Med Q3 Max
>> -4.17921640 -0.49875991 0.08672984 0.59020542 4.54644432
>>
>> Number of Observations: 14385
>> Number of Groups: 83
>>
>> I would like to summarize the fixed effects in terms of
>> miles rather
>> than log(miles + 1). How can that be done? Are there
>> common
>> generalized linear mixed models for miles driven that would
>> avoid the
>> transformation and allow effects to be presented in miles?
>>
>> thanks,
>>
>> Chuck
>>
>> --
>> Chuck Cleland, Ph.D.
>> NDRI, Inc. (www.ndri.org)
>> 71 West 23rd Street, 8th floor
>> New York, NY 10010
>> tel: (212) 845-4495 (Tu, Th)
>> tel: (732) 512-0171 (M, W, F)
>> fax: (917) 438-0894
>>
>>
>>
>> ------------------------------
>>
>> Message: 6
>> Date: Mon, 30 Mar 2009 20:26:38 +0200
>> From: Dimitris Rizopoulos <d.rizopoulos at erasmusmc.nl>
>> Subject: Re: [R-sig-ME] Mixed Model for Travel Distance
>> To: Chuck Cleland <ccleland at optonline.net>
>> Cc: r-sig-mixed-models at r-project.org
>> Message-ID: <49D10EDE.8050501 at erasmusmc.nl>
>> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>>
>> well, if you're only interested in the fixed effects,
>> then you can also
>> use a Generalized Estimating Equations approach that does
>> not make a
>> parametric assumption for the distribution of your error
>> terms, e.g.,
>> have a look at the 'geepack' package. Furthermore
>> and in case it is
>> relevant for your application, in GEE the estimated
>> parameters will have
>> a population interpretation, contrary to the GLMMs approach
>> in which
>> they will have a conditional on the random effects
>> interpretation.
>>
>>
>> I hope it helps.
>>
>> Best,
>> Dimitris
>>
>>
>> Chuck Cleland wrote:
>>> Hello:
>>> I am attempting to model the distance that clients
>> travel to a
>>> treatment program. There are 14385 clients nested in
>> 83 treatment
>>> programs (the grouping factor). The raw data are in
>> miles driven from
>>> the client's residence to the treatment program.
>> A natural logarithm
>>> transformation of miles driven works well to reduce
>> the positive skew in
>>> miles driven. I fit a model with lme() that looks
>> like this:
>>>
>>> Linear mixed-effects model fit by REML
>>> Data: dist.df
>>> AIC BIC logLik
>>> 38145.37 38319.54 -19049.68
>>>
>>> Random effects:
>>> Formula: ~1 | PROGRAM
>>> (Intercept) Residual
>>> StdDev: 0.4268969 0.8988483
>>>
>>> Fixed effects: log(DIST5DZ + 1) ~ QUAD + BEAL_TRI +
>> log(RZIPAREA + 1) +
>>> log(PZIPAREA + 1) + AGE.TRI + P3GEND + P5RACEX + EMPLD
>> + P13REASN +
>>> METHFST + URGE.DI + WDRAW.DI + RX_30 + P7HR30
>>>
>>> Value Std.Error DF
>> t-value p-value
>>> (Intercept) 1.2235603 0.13153231 14288
>> 9.30236 0.0000
>>> QUADSouthEast 0.2100666 0.13200891 76
>> 1.59131 0.1157
>>> QUADMidWest 0.2760390 0.15709516 76
>> 1.75715 0.0829
>>> QUADWest -0.1655914 0.15536003 76
>> -1.06586 0.2899
>>> BEAL_TRI250K-1M -0.0264939 0.11724713 76
>> -0.22597 0.8218
>>> BEAL_TRI<250K -0.0965256 0.16399464
>> 76 -0.58859 0.5579
>>> log(RZIPAREA + 1) 0.2965304 0.00757138 14288
>> 39.16463 0.0000
>>> log(PZIPAREA + 1) -0.0042061 0.04413826 76
>> -0.09529 0.9243
>>> AGE.TRI30-43 -0.0309444 0.01789442 14288
>> -1.72927 0.0838
>>> AGE.TRI43-83 -0.1281177 0.02168648 14288
>> -5.90772 0.0000
>>> P3GENDFemale -0.0195289 0.01632703 14288
>> -1.19611 0.2317
>>> P5RACEXLatino -0.3527584 0.02904416 14288
>> -12.14559 0.0000
>>> P5RACEXBlack -0.5485861 0.03306146 14288
>> -16.59292 0.0000
>>> P5RACEXOther -0.1580669 0.04811350 14288
>> -3.28529 0.0010
>>> EMPLDYes 0.0098856 0.01650635 14288
>> 0.59890 0.5493
>>> P13REASNYes -0.0095057 0.01650128 14288
>> -0.57606 0.5646
>>> METHFSTYes 0.0073478 0.01672948 14288
>> 0.43921 0.6605
>>> URGE.DI Strong-VeryStrong -0.0272769 0.02400068 14288
>> -1.13651 0.2558
>>> WDRAW.DISevere-VerySevere 0.0012054 0.01810067 14288
>> 0.06659 0.9469
>>> RX_30Yes 0.0934411 0.02165389 14288
>> 4.31521 0.0000
>>> P7HR30Yes -0.0408189 0.02256842 14288
>> -1.80867 0.0705
>>>
>>> Standardized Within-Group Residuals:
>>> Min Q1 Med Q3
>> Max
>>> -4.17921640 -0.49875991 0.08672984 0.59020542
>> 4.54644432
>>>
>>> Number of Observations: 14385
>>> Number of Groups: 83
>>>
>>> I would like to summarize the fixed effects in terms
>> of miles rather
>>> than log(miles + 1). How can that be done? Are there
>> common
>>> generalized linear mixed models for miles driven that
>> would avoid the
>>> transformation and allow effects to be presented in
>> miles?
>>>
>>> thanks,
>>>
>>> Chuck
>>>
>>
>> --
>> Dimitris Rizopoulos
>> Assistant Professor
>> Department of Biostatistics
>> Erasmus University Medical Center
>>
>> Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
>> Tel: +31/(0)10/7043478
>> Fax: +31/(0)10/7043014
>>
>>
>>
>> ------------------------------
>>
>> _______________________________________________
>> R-sig-mixed-models mailing list
>> R-sig-mixed-models at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>> End of R-sig-mixed-models Digest, Vol 27, Issue 36
>> **************************************************
>
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