random_list_sra | R Documentation |

Simulate sequential rank agreement from completely uninformative lists (ie., raw permutations of items) and compute the corresponding sequential rank agreement curves. The following attributes are copied from the input object: number of lists, number of items and amount of censoring.

```
random_list_sra(
object,
B = 1,
n = 1,
na.strings = NULL,
nitems = NULL,
type = c("sd", "mad"),
epsilon = 0
)
```

`object` |
A matrix of numbers or list of vectors representing ranked lists. |

`B` |
An integer giving the number of randomizations to sample over in the case of censored observations |

`n` |
Integer: the number of permutation runs. For each permutation run we permute each of the lists in object and compute corresponding the sequential rank agreement curves |

`na.strings` |
A vector of character values that represent censored observations |

`nitems` |
The total number of items in the original lists if we only have partial lists available. Will be derived from the unique elements of the object if set to |

`type` |
The type of measure to use. Either sd (standard deviation - the default) or mad (median absolute deviance) |

`epsilon` |
A non-negative numeric vector that contains the minimum limit in proportion of lists that must show the item. Defaults to 0. If a single number is provided then the value will be recycles to the number of items. Should usually be low. |

A matrix with n columns and the same number of rows as for the input object. Each column contains one simulated sequential rank agreement curve from one permutation run.

Claus Ekstrøm <ekstrom@sund.ku.dk>

```
# setting with 3 lists
mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
# compute sequential rank agreement of lists
sra(mlist)
# compute sequential rank agreement of 5 random permutations
random_list_sra(mlist, n=5)
```

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