| correlationTest {fBasics} | R Documentation |
Correlation tests
Description
Tests if two series are correlated.
Usage
correlationTest(x, y, method = c("pearson", "kendall", "spearman"),
title = NULL, description = NULL)
pearsonTest(x, y, title = NULL, description = NULL)
kendallTest(x, y, title = NULL, description = NULL)
spearmanTest(x, y, title = NULL, description = NULL)
Arguments
x, y |
numeric vectors of data values. |
method |
a character string naming which test should be applied. |
title |
an optional title string, if not specified the input's data name is deparsed. |
description |
optional description string, or a vector of character strings. |
Details
These functions test for association/correlation between paired samples based on the Pearson's product moment correlation coefficient (a.k.a. sample correlation), Kendall's tau, and Spearman's rho coefficients.
pearsonTest, kendallTest, and spearmanTest are
wrappers of base R's cor.test with simplified
interface. They provide 'exact' and approximate p-values for all
three alternatives (two-sided, less, and greater), as well as 95%
confidence intervals. This is particularly convenient in interactive
use.
Instead of calling the individual functions, one can use
correlationTest and specify the required test with argument
method.
Value
an object from class fHTEST
References
Conover, W. J. (1971); Practical nonparametric statistics, New York: John Wiley & Sons.
Lehmann E.L. (1986); Testing Statistical Hypotheses, John Wiley and Sons, New York.
See Also
locationTest,
scaleTest,
varianceTest.
Examples
## x, y -
x = rnorm(50)
y = rnorm(50)
## correlationTest -
correlationTest(x, y, "pearson")
correlationTest(x, y, "kendall")
spearmanTest(x, y)