assoc.twocont {descriptio} | R Documentation |

## Measures the association between two continuous variables

### Description

Measures the association between two continuous variables with Pearson, Spearman and Kendall correlations.

### Usage

```
assoc.twocont(x, y, weights = NULL, na.rm = FALSE,
nperm = NULL, distrib = "asympt")
```

### Arguments

`x` |
a continuous variable (must be a numeric vector) |

`y` |
a continuous variable (must be a numeric vector) |

`weights` |
numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used. |

`na.rm` |
logical, indicating whether NA values should be silently removed before the computation proceeds. Default is FALSE. |

`nperm` |
numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. |

`distrib` |
the null distribution of permutation test of independence can be approximated by its asymptotic distribution ( |

### Value

A data frame with Pearson, Spearman and Kendall correlations. The correlation value is in the first row and a p-value from a permutation (so non parametric) test of independence is in the second row.

### Author(s)

Nicolas Robette

### See Also

`assoc.twocat`

, `assoc.catcont`

, `assoc.yx`

, `condesc`

,
`catdesc`

, `darma`

### Examples

```
## Hollander & Wolfe (1973), p. 187f.
## Assessment of tuna quality. We compare the Hunter L measure of
## lightness to the averages of consumer panel scores (recoded as
## integer values from 1 to 6 and averaged over 80 such values) in
## 9 lots of canned tuna.
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
assoc.twocont(x,y,nperm=100)
```

*descriptio*version 1.3 Index]