rankL2R: Rank responses under the Bayesian framework according to the...

In RankResponse: Ranking Responses in a Single Response Question or a Multiple Response Question

Rank responses under the Bayesian framework according to the loss function in Method 3 of Wang and Huang (2004).

Description

Rank responses of a single response question or a multiple response question under the Bayesian framework according to the loss function in Method 3 of Wang and Huang (2004).

Usage

rankL2R(data, response.number, prior.parameter, e)

Arguments

data	A m by n matrix d_{ij}, where d_{ij} = 0 or 1. If the ith respondent selects the jth response, then d_{ij} = 1, otherwise d_{ij} = 0.
response.number	The number of the responses.
prior.parameter	The parameter vector of the Dirichlet prior distribution, where the vector dimension is 2^response.number.
e	A cut point used in the loss function which depends on the economic costs.

Value

The rankL2R returns the estimated probabilities of the responses being selected in the first line and the ranks of the responses in the second line.

Author(s)

Hsiuying Wang [e-mail] , Yu-Chun Lin [e-mail]

References

Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208.

See Also

rankLN, rank.wald, rank.gs

Examples

set.seed(12345)
# This is an example to rank k responses in a multiple response question
# when the number of respondents is 1000 and the value e is 0.15.
# In this example, we do not use a real data, but generate data in the first six lines.
k <- 3
data <- matrix(NA, nrow = 1000, ncol = k)
for(i in 1:k){
  p <- runif(1)
  data[, i] <- sample(c(0, 1), 1000, p = c(p, 1-p), replace = TRUE)
}
## or upload the true data
response.number <- 3
prior.parameter <- c(5, 98, 63, 7, 42, 7, 7, 7)
e <- 0.15
rankL2R(data, response.number, prior.parameter, e)

RankResponse documentation built on May 11, 2022, 5:18 p.m.