Did you read the help for cor.test? Test statistics, references....
looks pretty complete to me. If the descriptions are too terse,
then the references given would be the next step.
Sarah
Excerpted from ?cor.test
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 rank-based 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
p-value 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, p-values are computed using algorithm AS 89.
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).
On Fri, Mar 13, 2009 at 5:37 AM, mentor_ <mentor_ at gmx.net>
wrote:>
> Hi,
>
> I am not sure which test is applied to the data if you use cor.test(x, y) ?
> Is it an unpaired t-Test?
>
>
> Regards
--
Sarah Goslee
http://www.functionaldiversity.org