This function calculates the exact or approximate MLE of a Mallows-Binomial distribution using a user-specified method.

fit_mb(
  rankings,
  ratings,
  M,
  method = c("ASTAR", "Greedy", "GreedyLocal", "FV")
)

Arguments

rankings

A matrix of rankings, potentially with attribute "assignments" to signify separate reviewer assignments. One ranking per row.

ratings

A matrix of ratings, one row per judge and one column per object.

M

Numeric specifying maximum (=worst quality) integer rating.

method

A character string indicating which estimation method to use when estimating parameters. Allowable options are currently "ASTAR", "Greedy", "GreedyLocal", and "FV". Defaults to exact search, "ASTAR".

Value

A list with elements pi0, the estimated consensus ranking MLE, p, the estimated object quality parameter MLE, theta, the estimated scale parameter MLE, and numnodes, number of nodes traversed during algorithm and a measure of computational complexity. If multiple MLEs are found, pi0, p, and theta are returned a matrix elements, with one row per MLE.

Examples

data("ToyData1")
fit_mb(ToyData1$rankings,ToyData1$ratings,ToyData1$M,method="ASTAR")
#> $pi0
#> [1] 1 2 3
#> 
#> $p
#> [1] 0.125 0.125 0.750
#> 
#> $theta
#> [1] 1e+08
#> 
#> $num_nodes
#> [1] 5
#> 
fit_mb(ToyData1$rankings,ToyData1$ratings,ToyData1$M,method="Greedy")
#> $pi0
#> [1] 1 2 3
#> 
#> $p
#> [1] 0.125 0.125 0.750
#> 
#> $theta
#> [1] 1e+08
#> 
#> $num_nodes
#> [1] 6
#> 
fit_mb(ToyData1$rankings,ToyData1$ratings,ToyData1$M,method="GreedyLocal")
#> $pi0
#> [1] 1 2 3
#> 
#> $p
#> [1] 0.125 0.125 0.750
#> 
#> $theta
#> [1] 1e+08
#> 
#> $num_nodes
#> [1] 9
#> 
fit_mb(ToyData1$rankings,ToyData1$ratings,ToyData1$M,method="FV")
#> $pi0
#> [1] 1 2 3
#> 
#> $p
#> [1] 0.125 0.125 0.750
#> 
#> $theta
#> [1] 1e+08
#> 
#> $num_nodes
#> [1] 2
#>