Test for Association/Correlation Between Paired Samples
Description
Test for association between paired samples, using one of Pearson's product moment correlation coefficient, Kendall's tau or Spearman's rho.
Usage
1 2 3 4 5 6 7 8 9 10 
Arguments
x, y 
numeric vectors of data values. 
alternative 
indicates the alternative hypothesis and must be
one of 
method 
a character string indicating which correlation
coefficient is to be used for the test. One of 
exact 
a logical indicating whether an exact pvalue should be
computed. Used for Kendall's tau and
Spearman's rho.
See ‘Details’ for the meaning of 
conf.level 
confidence level for the returned confidence interval. Currently only used for the Pearson product moment correlation coefficient if there are at least 4 complete pairs of observations. 
continuity 
logical: if true, a continuity correction is used for Kendall's tau and Spearman's rho when not computed exactly. 
formula 
a formula of the form 
data 
an optional matrix or data frame (or similar: see

subset 
an optional vector specifying a subset of observations to be used. 
na.action 
a function which indicates what should happen when
the data contain 
... 
further arguments to be passed to or from methods. 
Details
The three methods each estimate the association between paired samples and compute a test of the value being zero. They use different measures of association, all in the range [1, 1] with 0 indicating no association. These are sometimes referred to as tests of no correlation, but that term is often confined to the default method.
If method
is "pearson"
, the test statistic is based on
Pearson's product moment correlation coefficient cor(x, y)
and
follows a t distribution with length(x)2
degrees of freedom
if the samples follow independent normal distributions. If there are
at least 4 complete pairs of observation, an asymptotic confidence
interval is given based on Fisher's Z transform.
If method
is "kendall"
or "spearman"
, Kendall's
tau or Spearman's rho statistic is used to
estimate a rankbased measure of association. These tests may be used
if the data do not necessarily come from a bivariate normal
distribution.
For Kendall's test, by default (if exact
is NULL), an exact
pvalue is computed if there are less than 50 paired samples containing
finite values and there are no ties. Otherwise, the test statistic is
the estimate scaled to zero mean and unit variance, and is approximately
normally distributed.
For Spearman's test, pvalues are computed using algorithm AS 89 for
n < 1290 and exact = TRUE
, otherwise via the asymptotic
t approximation. Note that these are ‘exact’ for n
< 10, and use an Edgeworth series approximation for larger sample
sizes (the cutoff has been changed from the original paper).
Value
A list with class "htest"
containing the following components:
statistic 
the value of the test statistic. 
parameter 
the degrees of freedom of the test statistic in the case that it follows a t distribution. 
p.value 
the pvalue of the test. 
estimate 
the estimated measure of association, with name

null.value 
the value of the association measure under the
null hypothesis, always 
alternative 
a character string describing the alternative hypothesis. 
method 
a character string indicating how the association was measured. 
data.name 
a character string giving the names of the data. 
conf.int 
a confidence interval for the measure of association. Currently only given for Pearson's product moment correlation coefficient in case of at least 4 complete pairs of observations. 
References
D. J. Best & D. E. Roberts (1975), Algorithm AS 89: The Upper Tail Probabilities of Spearman's rho. Applied Statistics, 24, 377–379.
Myles Hollander & Douglas A. Wolfe (1973), Nonparametric Statistical Methods. New York: John Wiley & Sons. Pages 185–194 (Kendall and Spearman tests).
See Also
Kendall
in package Kendall.
pKendall
and
pSpearman
in package
SuppDists,
spearman.test
in package
pspearman,
which supply different (and often more accurate) approximations.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Hollander & Wolfe (1973), p. 187f.
## Assessment of tuna quality. We compare the Hunter L measure of
## lightness to the averages of consumer panel scores (recoded as
## integer values from 1 to 6 and averaged over 80 such values) in
## 9 lots of canned tuna.
x < c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y < c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
## The alternative hypothesis of interest is that the
## Hunter L value is positively associated with the panel score.
cor.test(x, y, method = "kendall", alternative = "greater")
## => p=0.05972
cor.test(x, y, method = "kendall", alternative = "greater",
exact = FALSE) # using large sample approximation
## => p=0.04765
## Compare this to
cor.test(x, y, method = "spearm", alternative = "g")
cor.test(x, y, alternative = "g")
## Formula interface.
require(graphics)
pairs(USJudgeRatings)
cor.test(~ CONT + INTG, data = USJudgeRatings)
