[R-sig-ME] Single DV with multiple measures for time-varying IV?
Ben Bolker
bbolker @ending from gm@il@com
Wed Nov 21 18:42:49 CET 2018
Just for the record; I agree that it's almost definitely *not* going
to work to have identical responses for different predictor values.
Someone asked a similar question on StackOverflow recently:
https://stackoverflow.com/questions/53034261/warning-lme4-model-failed-to-converge-with-maxgrad
cheers
Ben Bolker
On 2018-11-21 10:28 a.m., Pero, Ellen wrote:
> Thank you Bill and Thierry.
>
>
> I don't yet have data in hand (cortisol samples await assay). However, this is what they will look like:
>
> cortisol
> ---------------------------------
> ID DV Month 1 Month 2 ... Month 8 dam age, sire age, calf birthdate
> 1 ....
> 2 ....
> .. ....
> 60 ....
>
> While I can simulate more data, my primary question is theoretical:
>
> Is it acceptable practice to share a single dependent response (DV: here calf mass (kg)) amongst multiple time-varying nested independent predictors (here, monthly cortisol) as long as I place a random effect to signify the individual I am nesting on (ID).
>
>
>
> ID DV cortisol, time, dam age, sire age, calf birthdate
> 1 17 35 Month 1 4 3 140
> 1 17 42 Month 2 4 3 140
> ........................................................
> 1 17 58 Month 8 4 3 140
>
> 2 19 30 Month 1 3 5 150
> 2 19 33 Month 2 3 5 150
> ........................................................
> 2 19 42 Month 7 3 5 150
>
> ........................................................
>
> 60 14 51 Month 2 2 2 162
> 60 14 58 Month 3 2 2 162
> ........................................................
> 60 14 70 Month 8 2 2 162
>
> From my digging, I don't think it is good practice. So, for now, I am planning to average repeated cortisol samples within an individual to produce an 'early' and 'late' value, and include both as covariates within a glm.
>
>
> I appreciate your support and encouragement!
>
> El
>
>
> Ellen Pero
> PhD Student
> Wildlife Biology Program
> W.A. Franke College of Forestry and Conservation
> University of Montana
> 32 Campus Drive, FOR 318
> Missoula, MT 59812
>
>
>
> ________________________________
> From: Bill Poling <Bill.Poling using zelis.com>
> Sent: Monday, November 19, 2018 4:26 AM
> To: Pero, Ellen
> Cc: Thierry Onkelinx; r-sig-mixed-models using r-project.org; Bill Poling
> Subject: RE: [R-sig-ME] Single DV with multiple measures for time-varying IV?
>
> Hi Ellen.
>
> If the data frame is not too terribly large, a dput() would be useful.
> See ?dput()
> Or a str() would help as well
> See ?str()
> However, as Thierry suggests a subset of your data would be most helpful.
>
> I will be interested to follow this topic as I am teaching myself R and learning the various modeling methods and their purposes along the way.
>
> I think you will gain considerable support from this list relevant to your topic.
>
> Best regards.
>
> WHP
>
>
> From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Thierry Onkelinx via R-sig-mixed-models
> Sent: Monday, November 19, 2018 4:01 AM
> To: ellen.pero using umconnect.umt.edu
> Cc: r-sig-mixed-models <r-sig-mixed-models using r-project.org>
> Subject: Re: [R-sig-ME] Single DV with multiple measures for time-varying IV?
>
> Dear Ellen,
>
> An extract of your dataset or a small dummy dataset coverting the important
> features of your data would make it much easier to answer your questions.
> And please don't send HTML emails. Any HTML formating gets stripped which
> can make your email very hard to read.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> mailto:thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> http://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
> ///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op ma 12 nov. 2018 om 20:01 schreef Pero, Ellen <
> mailto:ellen.pero using umconnect.umt.edu>:
>
>> Hi all:
>>
>> I have an analytical dilemma wherein I have a single DV with multiple
>> categorical and continuous IVs (one of which is a continuous IV that has
>> multiple measurements across time). I'm not sure the best way to model for
>> this - though it's clearly a hierarchical situation so I thought this might
>> be a good venue to pose the question.
>>
>> Specifically, I have 60 pregnant elk from which I took monthly cortisol
>> samples across gestation (some missing values, so 5-8 samples/female across
>> gestation). I'm interested in how those stress measurements across
>> gestation (along with a range of other IVs that don't vary with time, e.g.,
>> dam age, sire age, calf birthdate) influence the birth mass of each
>> female's calf.
>>
>> Any suggestions on analysis for situations where a single DV is predicted
>> by longitudinal measures of time-varying IV (along with non-varying IVs)?
>>
>> I'm new to this list and will spend some time familiarizing myself with it
>> - but was eager to get my question out. Apologies if this isn't the right
>> venue for my non-development related question. Please disregard if
>> appropriate.
>>
>> I appreciate any thoughts/advice/suggestions!
>> El
>>
>>
>>
>> Ellen Pero
>> PhD Student
>> Wildlife Biology Program
>> W.A. Franke College of Forestry and Conservation
>> University of Montana
>> 32 Campus Drive, FOR 318
>> Missoula, MT 59812
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
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