wdbm: Weighted Distance-based Models

In pmr: Probability Models for Ranking Data

Weighted Distance-based Models

Description

Weighted Distance-based Models for ranking data. The distance-based models assume that rankings closer to the modal ranking are more likely to be observed. Weighted distance-based models are extensions of distance-based models with by allowing weights for different items.

Usage

wdbm(dset, dtype="tau")

Arguments

dset	a ranking dataset (aggregated)
dtype	type of weighted distance between two rankings. tau : Kendall's tau, rho : Spearman's rho, rho2 : Spearman's rho square, foot : footrule

Details

Fit the weighted distance-based models for the dataset and return a mle object. Standard methods on mle (e.g., @coef, @vcov) apply. The modal ranking and the Chi-square residual are given in the output.

Author(s)

Paul H. Lee and Philip L. H. Yu

References

Lee, P. H., and Yu, P. L. H. (2010) Distance-based tree models for ranking data. Computational Statistics and Data Analysis, 54(6), 1672-1682.

Lee, P. H., and Yu, P. L. H. (2012) Mixtures of weighted distance-based models for ranking data with applications in political studies. Computational Statistics and Data Analysis, 56(8), 2486-2500.

See Also

dbm

Examples

## create an artificial dataset
X1 <- c(1,1,2,2,3,3)
X2 <- c(2,3,1,3,1,2)
X3 <- c(3,2,3,1,2,1)
n <- c(6,5,4,3,2,1)
test <- data.frame(X1,X2,X3,n)

## fit the weighted distance-based model with Spearman's foot distance
## wdbm(test,dtype="foot")

pmr documentation built on June 24, 2022, 5:06 p.m.