rol: The Rank-ordered Logit Models

In pmr: Probability Models for Ranking Data

The Rank-ordered Logit Models

Description

The Rank-ordered Logit (ROL) Models for ranking data. ROL models are extensions of the Luce models by incorporating covariates.

Usage

rol(dset, covariate)

Arguments

dset	a ranking dataset
covariate	the covariates of the ranking dataset

Details

Fit the rank-ordered logit models for the dataset and return a mle object. Standard methods on mle (e.g., @coef, @vcov) apply. By default, the intercept term is included.

Author(s)

Paul H. Lee and Philip L. H. Yu

References

Beggs, S., Cardell, S., and Hausman, J. (1981) Assessing the potential demand for electric cars. Journal of Econometrics, 16: 1-19.

Chapman, R. G., and Staelin, R. (1982) Exploiting rank ordered choice set data within the stochastic utility model. Journal of Market Research, 19:288-301.

Hausman, J., and Ruud, P. A. (1987) Specifying and testing econometric models for rank-ordered data. Journal of Econometrics, 34:83-104.

See Also

pl

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)
X4 <- c(6,5,4,3,2,1)
test <- data.frame(X1,X2,X3)

## fit the Luce model
## rol(test,X4)

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