View source: R/covariance_and_correlation_related_functions.R

Correlation significance testing using Fisher's z-transformation | R Documentation |

Correlation significance testing using Fisher's z-transformation.

```
cor_test(y, x, type = "pearson", rho = 0, a = 0.05 )
```

`y` |
A numerical vector. |

`x` |
A numerical vector. |

`type` |
The type of correlation you want. "pearson" and "spearman" are the two supported types because their standard error is easily calculated. |

`rho` |
The value of the hypothesised correlation to be used in the hypothesis testing. |

`a` |
The significance level used for the confidence intervals. |

The function uses the built-in function "cor" which is very fast, then computes a confidence interval and produces a p-value for the hypothesis test.

A vector with 5 numbers; the correlation, the p-value for the hypothesis test that each of them is
equal to "rho", the test statistic and the `a/2\%`

lower and upper confidence limits.

Michail Tsagris.

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

```
allbetas, univglms
```

```
x <- rcauchy(60)
y <- rnorm(60)
cor_test(y, x)
```

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