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calculating correlation coefficients on repeated measures

2 messages · Keith Larson, Sarah Goslee

Keith Larson
# 
Dear list,

I have 9 repeated measures (measurement variable ==  'Delta13C') for
individuals (ID variable == 'Individual_ID'. Each repeated measure is
"indexed" (right term?) by the variable 'FeatherPosition' and given as
c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9'). I would like
to calculate a correlation coefficient (r) and p.value for all
measures of 'Delta13C' by individual. the function 'cor' only seems to
work when comparing two individual measures (e.g. P1 and P2, P2 and
P3, etc.) and only if I restructure my table. Any suggestions:

In SAS with 'proc corr' I would like results that look like:

Individual ID, r, p
WW_08I_01,-0.03,0.94
WW_08I_03,0.53,0.14

Trying to get started in R!
Keith

Sample dataset:

WW_Sample_SI <-
structure(list(Individual_ID = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("WW_08I_01",
"WW_08I_03"), class = "factor"), FeatherPosition = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L), .Label = c("P1", "P2", "P3", "P4", "P5", "P6", "P7", "P8",
"P9"), class = "factor"), Delta13C = c(-18.3, -18.53, -19.55,
-20.18, -20.96, -21.08, -21.5, -17.42, -13.18, -22.3, -22.2,
-22.18, -22.14, -21.55, -20.85, -23.1, -20.75, -20.9)), .Names =
c("Individual_ID",
"FeatherPosition", "Delta13C"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18"))

*******************************************************************************************
Keith Larson, PhD Student
Evolutionary Ecology, Lund University
S?lvegatan 37
223 62 Lund Sweden
Phone: +46 (0)46 2229014 Mobile: +46 (0)73 0465016 Fax: +46 (0)46 2224716
Skype: sternacaspia FB: keith.w.larson at gmail.com
Sarah Goslee
# 
Hi Keith,

You do need to reorganize your data. cor() will work on any number of variables
as long as they are columns in a matrix or data frame.

There are a lot of ways to reorganize data, of various power and complexity.
Here's one simple way:
P1     P2     P3     P4     P5     P6    P7     P8     P9
WW_08I_01 -18.3 -18.53 -19.55 -20.18 -20.96 -21.08 -21.5 -17.42 -13.18
WW_08I_03 -22.3 -22.20 -22.18 -22.14 -21.55 -20.85 -23.1 -20.75 -20.90
P1 P2 P3 P4 P5 P6 P7 P8 P9
P1  1  1  1  1  1 -1  1  1  1
P2  1  1  1  1  1 -1  1  1  1
P3  1  1  1  1  1 -1  1  1  1
P4  1  1  1  1  1 -1  1  1  1
P5  1  1  1  1  1 -1  1  1  1
P6 -1 -1 -1 -1 -1  1 -1 -1 -1
P7  1  1  1  1  1 -1  1  1  1
P8  1  1  1  1  1 -1  1  1  1
P9  1  1  1  1  1 -1  1  1  1

(With only two values, the correlation table is rather useless, but enough to
give the idea.)

However, cor.test() is what you'd need for significance testing, and it only
works on one pair of variables at a time. It's still easier to put them into
separate columns.
Sarah
On Mon, Dec 19, 2011 at 1:23 AM, Keith Larson <keith.larson at biol.lu.se> wrote: