Single DV with multiple measures for time-varying IV?

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


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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


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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.

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

  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

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 at r-project.org> On

Behalf Of Thierry Onkelinx via R-sig-mixed-models

Sent: Monday, November 19, 2018 4:01 AM
To: ellen.pero at umconnect.umt.edu
Cc: r-sig-mixed-models <r-sig-mixed-models at 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 at 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 at 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


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