calculating correlation coefficients on repeated measures
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:
library(ecodist) WW_Sample_table <- with(WW_Sample_SI, crosstab(Individual_ID, FeatherPosition, Delta13C)) WW_Sample_table
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
cor(WW_Sample_table)
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.
WW_Sample_table <- data.frame(WW_Sample_table) with(WW_Sample_table, cor.test(P1, P2))
Sarah
On Mon, Dec 19, 2011 at 1:23 AM, Keith Larson <keith.larson at biol.lu.se> wrote:
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"))
Sarah Goslee http://www.functionaldiversity.org