Correlation tests

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.

Examples

# \dontshow{
set.seed(1234)
# }
## x, y -
   x = rnorm(50)
   y = rnorm(50)
  
## correlationTest - 
   correlationTest(x, y, "pearson")
#> 
#> Title:
#>  Pearson's Correlation Test
#> 
#> Test Results:
#>   PARAMETER:
#>     Degrees of Freedom: 48
#>   SAMPLE ESTIMATES:
#>     Correlation: 0.267
#>   STATISTIC:
#>     t: 1.9199
#>   P VALUE:
#>     Alternative Two-Sided: 0.06082 
#>     Alternative      Less: 0.9696 
#>     Alternative   Greater: 0.03041 
#>   CONFIDENCE INTERVAL:
#>     Two-Sided: -0.0122, 0.5077
#>          Less: -1, 0.4728
#>       Greater: 0.0337, 1
#> 
   correlationTest(x, y, "kendall")
#> 
#> Title:
#>  Kendall's tau Correlation Test
#> 
#> Test Results:
#>   SAMPLE ESTIMATES:
#>     tau: 0.1559
#>   STATISTIC:
#>     z: 1.5977
#>     T | Exact: 708
#>   P VALUE:
#>     Alternative         Two-Sided: 0.1101 
#>     Alternative Two-Sided | Exact: 0.1123 
#>     Alternative              Less: 0.9449 
#>     Alternative      Less | Exact: 0.9457 
#>     Alternative           Greater: 0.05506 
#>     Alternative   Greater | Exact: 0.05615 
#> 
   spearmanTest(x, y)
#> 
#> Title:
#>  Spearman's rho Correlation Test
#> 
#> Test Results:
#>   SAMPLE ESTIMATES:
#>     rho: 0.2346
#>   STATISTIC:
#>     S: 15940
#>   P VALUE:
#>     Alternative Two-Sided: 0.1011 
#>     Alternative      Less: 0.9495 
#>     Alternative   Greater: 0.05053 
#>