Asymptotic p-value for many correlation coefficients {corrfuns} | R Documentation |

## Asymptotic p-value for many correlation coefficients

### Description

Asymptotic p-value for many correlation coefficients.

### Usage

```
correls(y, x, type = "pearson", rho = 0, alpha = 0.05)
```

### Arguments

`y` |
A numerical vector. |

`x` |
A numerical vector. |

`type` |
The type of correlation coefficient to compute, "pearson" or "spearman". |

`rho` |
The hypothesized value of the true partial correlation. |

`alpha` |
The significance level. |

### Details

Suppose you have a (dependent) variable `Y`

and a matrix of `p`

variables `\bf X`

and you want to get all the correlations between `Y`

and `X_i`

for `i=1,\ldots,p`

. if you type cor(y, x) in you will get a vector of the correlations. What I offer here is confidence interval for each of the correlations, the test statistic and the p-values for the hypothesis that each of them is equal to some value `\rho`

. The p-values and test statistics are useful for meta-analysis for example, combination of the p-values in one or even to see the false discovery rate (see the package **fdrtool** by Korbinian Strimmer).

### Value

A matrix with 5 columns, the correlations, the test statistics, their associated p-values and the relevant `(1-\alpha)\%`

confidence intervals.

### Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

### See Also

### Examples

```
y <- rnorm(40)
x <- matrix(rnorm(40 * 1000), ncol = 1000)
a <- correls(y, x )
```

*corrfuns*version 1.0 Index]