fillArow: Impute a row of ranks using the existing values of rankings
In crank: Completing Ranks

Description Usage Arguments Details Value Author(s) References See Also Examples

Imputes a row of missing ranks using the Lim-Wolfe procedure

1	fillArow(x,ranksums=NA,Arow,maxcon=TRUE)

`x`	A matrix of ranks that may contain ties and NAs. Columns represent objects ranked and rows represent ranking methods.
`ranksums`	The sums of ranks of all complete rows in x.
`Arow`	The row of x that is to be completed.
`maxcon`	Whether to impute rankings maximally consistent with the existing ones (TRUE) or minimally consistent (FALSE).

fillArow imputes missing ranks in the row designated by Arow using the information in ranksums. If the ranks already completed provide information on the order of imputation, that is used directly for imputed ranks of maximal consistency or inversely for imputed ranks of minimal consistency. If the existing ranks do not provide such information, the missing ranks are permuted, and a list of matrices with all the permutations is substituted. This may involve a recursive call to fillArow and produce a nested list of matrices. See Lim and Wolfe (2002) for details of this process.

The matrix x with row Arow completed or a list of such matrices, possibly nested.

Jim Lemon

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.

lwscreen, listBuilder, fillArows

 # 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 the second row with maximal consistency
 fillArow(lwmat,lwmat[1,],2)
 # now with minimal consistency
 fillArow(lwmat,lwmat[1,],2,maxcon=FALSE)