Below is a somewhat more general version of David's function,
which allows a choice of the association statistic from
vcd::assocstats(). Of course, only Cramer's V is calculated
on a scale of 0-1 for an absolute-value measure of strength
of association, but this could be accommodated by scaling to
diagonals = 1.
The OP specified binary variables, so tetrachoric correlations
might be more appropriate here. John Fox's polycor package
provides a more general approach to this problem, including
polychoric and polyserial correlations, as well as a hetcor()
function to calculate correlation-like measures for mixtures
of different variable types, all providing standard errors
and therefore the possibility to compute p-values.
catcor <- function(x, type=c("cramer", "phi", "contingency")) {
require(vcd)
nc <- ncol(x)
v <- expand.grid(1:nc, 1:nc)
type <- match.arg(type)
res <- matrix(mapply(function(i1, i2) assocstats(table(x[,i1],
x[,i2]))[[type]], v[,1], v[,2]), nc, nc)
rownames(res) <- colnames(res) <- colnames(x)
res
}
e.g.
dat <- data.frame(
v1=sample(LETTERS[1:5], 15, replace=TRUE),
v2=sample(LETTERS[1:5], 15, replace=TRUE),
v3=sample(LETTERS[1:5], 15, replace=TRUE))
> catcor(dat, type="phi")
v1 v2 v3
v1 2.000000 1.073675 0.942809
v2 1.073675 2.000000 1.105542
v3 0.942809 1.105542 2.000000
> catcor(dat, type="cramer")
v1 v2 v3
v1 1.0000000 0.5368374 0.4714045
v2 0.5368374 1.0000000 0.5527708
v3 0.4714045 0.5527708 1.0000000
> catcor(dat, type="contingency")
v1 v2 v3
v1 0.8944272 0.7317676 0.6859943
v2 0.7317676 0.8944272 0.7416198
v3 0.6859943 0.7416198 0.8944272
>
On 2/18/2014 9:38 AM, David Carlson wrote:
You might modify this function which computes Cramer's V using
the assocstats() function in package vcd:
catcor <- function(x) {
require(vcd)
nc <- ncol(x)
v <- expand.grid(1:nc, 1:nc)
matrix(mapply(function(i1, i2) assocstats(table(x[,i1],
x[,i2]))$cramer, v[,1], v[,2]), nc, nc)
}
e.g.
dat <- data.frame(v1=sample(LETTERS[1:5], 15, replace=TRUE),
+ v2=sample(LETTERS[1:5], 15, replace=TRUE),
+ v3=sample(LETTERS[1:5], 15, replace=TRUE))
catcor(dat)
[,1] [,2] [,3]
[1,] 1.0000000 0.5633481 0.5773503
[2,] 0.5633481 1.0000000 0.6831301
[3,] 0.5773503 0.6831301 1.0000000
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of Skála, Zdenek
(INCOMA GfK)
Sent: Tuesday, February 18, 2014 3:33 AM
To: r-help@r-project.org
Subject: [R] association of multiple variables
Dear all,
Please, is there a way in R to calculate association statistics
over more than 2 categorical (binary) variables?
I mean something similar what
cor(my.dataframe)
does for continuous variables, i.e. to have a matrix of
statistics and/or p-values as an output.
Many thanks!
Zdenek
- -
Zdenlk Skala
INCOMA GfK
[[alternative HTML version deleted]]
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