________________________________
From: Ebert,Timothy Aaron <tebert at ufl.edu>
Sent: Tuesday, January 31, 2023 9:50 AM
To: Carolyn J Miller <cjmill04 at syr.edu>; PIKAL Petr <petr.pikal at precheza.cz>; r-help at r-project.org <r-help at 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 at syr.edu>
Sent: Tuesday, January 31, 2023 9:30 AM
To: PIKAL Petr <petr.pikal at precheza.cz>; r-help at r-project.org; Ebert,Timothy Aaron <tebert at 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 at r-project.org<mailto:r-help at 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 at r-project.org<mailto:r-help-bounces at r-project.org>> On Behalf Of Carolyn J Miller
via
R-help
Sent: Monday, January 30, 2023 7:16 PM
To: r-help at r-project.org<mailto:r-help at 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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