[R] question

Tue Jan 31 16:43:26 CET 2023

Hi Boris,

It's hair cortisol so it shouldn't have an effect. My study species are ungulates, which retain their coat through the winter into the spring shedding out around April/May so in theory these two sampling periods should provide the same results as hair cort provides an average of accumulated cort levels released into the hair over that growth period until they shed out. Of course the individuals that had hair collected in March instead of December have had longer to incorporate more cort levels into the hair collected in comparison to their conspecifics captured in December.

I had a repeated measures approach to this previously but due to missing data from uneven captures the model gets angry since there's only 2 levels of replication and many are not repeated at all. We're considering dividing up the dataset by season to eliminate the need for repeated measures. I've had it suggested that we should use the single measure of cort (which is what most individuals have) in both rows (March and December) based on this logic, and then just run the models as separate seasons.

I ran the t-test between the march and december cort samples and they are not representing the same information.

The joys of data analysis!

Thanks for your feedback,

Carolyn J. Miller
M.S. Student, Ecology
SUNY-ESF, Environmental Biology

________________________________
From: Boris Steipe <boris.steipe using utoronto.ca>
Sent: Tuesday, January 31, 2023 10:16 AM
To: Carolyn J Miller <cjmill04 using syr.edu>
Cc: r-help using r-project.org <r-help using r-project.org>
Subject: Re: [R] question

Perhaps, rather than looking to compress your observations into a single number, you could simply visualize what you observed: use a boxplot to show the March and December observations, and overlay the three animals that were recaptured as individual points, connected with a line.

Feel free to ask again if you are not sure how to do that.

Cheers,
Boris

PS. Lets hope that the capture did not stress them to the degree that their cortisol is elevated at recapture :-)

> On 2023-01-31, at 09:52, Carolyn J Miller via R-help <r-help using r-project.org> wrote:
>
> Thank you!
>
> Carolyn J. Miller
> M.S. Student, Ecology
> SUNY-ESF, Environmental Biology
>
>
> ________________________________
> From: Ebert,Timothy Aaron <tebert using ufl.edu>
> Sent: Tuesday, January 31, 2023 9:50 AM
> To: Carolyn J Miller <cjmill04 using syr.edu>; PIKAL Petr <petr.pikal using precheza.cz>; r-help using r-project.org <r-help using r-project.org>
> Subject: RE: question
>
>
> As indicated here: https://www.geeksforgeeks.org/compute-the-correlation-coefficient-value-between-two-vectors-in-r-programming-cor-function/
>
> The cor() function needs two vectors. The only way that works is if you are looking at the correlation between �Month� and �Cort.�
>
> If you interested in the correlation between  Cort measured in month 3 versus month 12 then you are not getting the right answer.
>
>
>
> Animal ID is not relevant in this analysis (as presented).
>
> The animals that have been measured twice would be a repeated measures analysis (by default) unless there is some reason to suspect that the six month lag is too long for an outcome in month 3 to influence the outcome in month 12. The remaining animals are an experimental design for avoiding a repeated measures analysis. This would be something like a t-test to determine if the animals in Month 3 are different than Month 12.
>
>
>
> Tim
>
>
>
> From: Carolyn J Miller <cjmill04 using syr.edu>
> Sent: Tuesday, January 31, 2023 9:30 AM
> To: PIKAL Petr <petr.pikal using precheza.cz>; r-help using r-project.org; Ebert,Timothy Aaron <tebert using ufl.edu>
> Subject: Re: question
>
>
>
> [External Email]
>
> Hi Timothy,
>
>
>
> Here's some example data that might help to demonstrate how the data currently looks.
>
>
>
> AnimalID
>
> Month
>
> Cort
>
> 1
>
> 12
>
> 0.00591
>
> 1
>
> 3
>
> 0.00583
>
> 2
>
> 3
>
> 0.005722
>
> 3
>
> 3
>
> 0.005838
>
> 4
>
> 3
>
> 0.005873
>
> 4
>
> 12
>
> 0.0059
>
> 5
>
> 3
>
> 0.005724
>
> 6
>
> 12
>
> 0.005924
>
> 7
>
> 12
>
> 0.005758
>
> 8
>
> 12
>
> 0.005901
>
> 9
>
> 12
>
> 0.005894
>
> 10
>
> 3
>
> 0.005731
>
> 11
>
> 3
>
> 0.005951
>
>
>
> So Animal ID represents individual, 3 or 12 for month represents either a March capture event or a December capture event and then the corresponding cort value (which I used a random number generator to create these values above). Petr, I was afraid of that response, that by using cor() I'm fundamentally just testing the correlation for the 3 individuals that have both March and December samples.
>
>
>
> If you guys have other thoughts I'd appreciate any suggestions.
>
>
>
> Thanks for your help and clarifying that for me.
>
>
>
> Carolyn J. Miller
>
> M.S. Student, Ecology
>
> SUNY-ESF, Environmental Biology
>
>
>
>
>
>
>
>
>
> ________________________________
>
> From: PIKAL Petr
> Sent: Tuesday, January 31, 2023 2:36 AM
> To: Carolyn J Miller; r-help using r-project.org<mailto:r-help using r-project.org>
> Subject: RE: question
>
>
>
> Hallo Carolyn
>
> From what you describe you cannot calculate correlations.
>
> You stated that you have two sets of data, one for December and one for
> March and that rows in one set is not related to the rows in another set and
> even persons tested in both months do not have their values on the same row.
> In that case cor is not appropriate. You should first adjust your data so
> that results of those 3 persons are on the same row but even after that only
> those 3 values could be evaluated by "cor".
>
> From what you wrote I think that t.test or similar beast is the way you
> should take.
>
> But without same data sample I may be wrong.
>
> Cheers
> Petr
>
>> -----Original Message-----
>> From: R-help <r-help-bounces using r-project.org<mailto:r-help-bounces using r-project.org>> On Behalf Of Carolyn J Miller
> via
>> R-help
>> Sent: Monday, January 30, 2023 7:16 PM
>> To: r-help using r-project.org<mailto:r-help using r-project.org>
>> Subject: [R] question
>>
>> Hi guys,
>>
>> I am using the cor() function to see if there are correlations between
> March
>> cortisol levels and December cortisol levels and I'm trying to figure out
> if the
>> function is doing what I want it to do.
>>
>> Each sample has it's own separate row in the CSV file that I'm working out
> of.
>> March Cort and December Cort are different columns and they come from
>> separate samples, therefore their values would not be on the same row.
> There
>> are only 3 individuals that have both December cort values and March
> cortisol
>> values but they still have different sample ID values (from different
> seasons) so
>> they are also not on the same row.
>>
>> I ran the function twice: once as cor(cortphcor, use = "complete.obs")
> first
>>
>> and then cor(cortphcor, use = "pairwise.complete.obs", method =
> "pearson").
>>
>> I received the same output both times. I guess what I'm asking is, is the
> output
>> simply the correlation just for those 3 samples or is the second pairwise.
>> complete.obs version giving me the correlation for all of the cort samples
> for
>> March against all of the samples for December despite not being on the
> same
>> row? I'm trying to figure out how many sample values are contributing to
> the
>> correlation results I'm getting.
>>
>> Thanks,
>>
>> Carolyn
>>
>>
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>>
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>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

--
Boris Steipe MD, PhD

Professor em.
Department of Biochemistry
Temerty Faculty of Medicine
University of Toronto

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