fillArows {crank} | R Documentation |

## Impute ranks using the existing values of rankings

### Description

Imputes missing ranks using the Lim-Wolfe procedure

### Usage

```
fillArows(x,maxcon=TRUE)
```

### Arguments

`x` |
A matrix of ranks that may contain ties and NAs. Columns represent objects ranked and rows represent ranking methods. |

`maxcon` |
Whether to impute rankings maximally consistent with the existing ones (TRUE) or minimally consistent (FALSE). |

### Details

‘fillArows’ imputes missing ranks by examining the completed ranks for each set of rows that have the same number of missing ranks. If more than one row has the minimum number of missing values, the order of these rows is permuted and the matrix ‘x’ becomes a list of matrices in which the values in the rows will be imputed in different orders. Another level of permutation and multiplication of matrices may occur in ‘fillArow’ to which the matrices are passed for the actual imputation. The function ‘getLWargs’ is called to get the arguments for ‘fillArow’. See Lim and Wolfe (2002) for details of this process.

### Value

A list of one or more completed matrices of ranks, possibly nested.

### Author(s)

Jim Lemon

### References

Lim, D.H. & Wolfe, D.A. (2002) An efficient alternative to average ranks for testing with incomplete ranking data. Biometrical Journal, 43(2): 187-206.

### See Also

### Examples

```
# The first example matrix from Lim and Wolfe (2002)
lwmat<-matrix(c(3,1,2,4,NA,2,1,NA,2,NA,1,NA),nrow=3,byrow=TRUE)
# complete with maximal consistency, permuting row order
fillArows(lwmat)
# now with minimal consistency as above
fillArows(lwmat,maxcon=FALSE)
```

*crank*version 1.1-2 Index]