# rankL2R: Rank responses under the Bayesian framework according to the... In RankResponse: Ranking Responses in a Single Response Question or a Multiple Response Question

 rankL2R R Documentation

## 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 wang@stat.nycu.edu.tw , Yu-Chun Lin restart79610@hotmail.com

### 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.

`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.