# correlation: Correlation with Relevance and Significance Measures In relevance: Calculate Relevance

## Description

Inference for a correlation coefficient: Collect quantities, including Relevance and Significance measures

## Usage

 ```1 2 3``` ```correlation(x, y = NULL, method = c("pearson", "spearman"), hypothesis = 0, testlevel=getOption("testlevel"), rlv.threshold=getOption("rlv.threshold"), ...) ```

## Arguments

 `x` data for the first variable, or matrix or data.frame containing both variables `y` data for the second variable `hypothesis` the null effect to be tested, and anchor for the relevance `method` type of correlation, either `"pearson"` for the ordinary Pearson product moment correlation, or `"spearman"` for the nonparametric measures `testlevel` level for the test, also determining the confidence level `rlv.threshold` Relevance threshold, or a vector of thresholds from which the element `corr` is taken
 `...` further arguments, ignored

## Value

an object of `class` `'inference'`, a vector with components

`effect`:

correlation, transformed with Fisher's z transformation

`ciLow, ciUp`:

confidence interval for the effect

`Rle, Rls, Rlp`:

relevance measures: estimated, secured, potential

`Sig0`:

significance measure for test or 0 effect

`Sigth`:

significance measure for test of `effect` == relevance threshold

`p.value`:

p value for test against 0

In addition, it has `attributes`

`method`:

type of correlation

`effectname`:

label for the effect

`hypothesis`:

the null effect

`n`:

number(s) of observations

`estimate`:

estimated correlation

`conf.int`:

confidence interval on correlation scale

`statistic`:

test statistic

`data: `

data.frame containing the two variables

`rlv.threshold`:

relevance threshold

Werner A. Stahel

## References

see those in `relevance-package`.

`cor.test`
 `1` ```correlation(iris[1:50,1:2]) ```