fit_RCBTL.RdThis function fits a Bayesian Rank-Clustered BTL model to ordinal comparison data (e.g., complete rankings, partial rankings, pairwise comparisons, or groupwise comparisons) such that the worth parameters are drawn from a PSSF prior (as defined in Pearce and Erosheva 2024).
fit_RCBTL(
Pi,
J,
a_gamma,
b_gamma,
lambda,
nu0 = NULL,
num_iters = 100,
nu_reps = 3,
groupwise = FALSE
)A matrix of preference orderings ("rankings"), such that the (i,j) entry is the index of the jth-most preferred object according to judge i. If groupwise=TRUE, then the index corresponds to the jth-most preferred object among those in row i; if groupwise=FALSE, it is assumed that all unranked objects (if any) are less preferred than those which are ranked.
A numeric indicating the total number of objects being compared.
A numeric for the first hyperparameter (shape) in a Gamma prior on each worth parameter.
A numeric for the second hyperparameter (rate) in a Gamma prior on each worth parameter.
A numeric for the Poisson hyperparameter on the number of non-empty clusters in the partition of worth parameters.
A numeric vector for the initialization of worth parameters, omega, in the MCMC algorithm. Default to NULL, indicating random initialization.
A numeric indicating the total number of outer MCMC iterations (i.e., the number of times the partition is updated in the Gibbs sampler).
A numeric indicating the number of times each worth parameter is drawn per update of the parameter partition. There will be a total of num_itersxnu_reps samples from the posterior.
A boolean to indicate whether the observed rankings are complete/partial rankings (FALSE; default) or groupwise comparisons (TRUE).
A list of 4 elements: omega, a (num_itersxnu_reps)xJ matrix of approximate posterior draws of the object-specific worth parameters, omega; nu a (num_itersxnu_reps)xJ matrix of the unique parameter values corresponding to the jth partition cluster in posterior draw i, g a a (num_itersxnu_reps)xJ matrix indicating the cluster membership of object j in posterior draw i, and K a vector of the number of non-empty partition clusters in each posterior draw.
Pi <- matrix(data=c(1,2,3,NA,NA,1,2,3,4,5),byrow=TRUE,nrow=2)
fit_RCBTL(Pi=Pi,J=5,a_gamma=5,b_gamma=3,lambda=2,num_iters=5,nu_reps=2)
#> $omega
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.6903096 2.167205 2.167205 2.6903096 2.167205
#> [2,] 2.4870672 1.020402 1.020402 2.4870672 1.020402
#> [3,] 2.5823728 1.022356 1.022356 2.5823728 1.022356
#> [4,] 1.1166087 1.257511 1.257511 1.1166087 1.257511
#> [5,] 1.5845342 2.490267 2.490267 0.8018633 2.490267
#> [6,] 1.0488350 1.791004 1.791004 1.3354488 1.791004
#> [7,] 3.4548519 1.676795 1.676795 2.1210863 1.676795
#> [8,] 1.2682114 3.282189 3.282189 1.4273731 3.282189
#> [9,] 0.9868771 1.739470 1.739470 0.8848360 1.739470
#> [10,] 2.3788136 2.747735 2.747735 1.0574828 2.747735
#>
#> $nu
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.1672053 2.6903096 NA NA NA
#> [2,] 1.0204022 2.4870672 NA NA NA
#> [3,] 1.0223556 2.5823728 NA NA NA
#> [4,] 1.2575105 1.1166087 NA NA NA
#> [5,] 1.5845342 0.8018633 2.490267 NA NA
#> [6,] 1.0488350 1.3354488 1.791004 NA NA
#> [7,] 3.4548519 2.1210863 1.676795 NA NA
#> [8,] 1.2682114 1.4273731 3.282189 NA NA
#> [9,] 0.9868771 0.8848360 1.739470 NA NA
#> [10,] 2.3788136 1.0574828 2.747735 NA NA
#>
#> $g
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2 1 1 2 1
#> [2,] 2 1 1 2 1
#> [3,] 2 1 1 2 1
#> [4,] 2 1 1 2 1
#> [5,] 1 3 3 2 3
#> [6,] 1 3 3 2 3
#> [7,] 1 3 3 2 3
#> [8,] 1 3 3 2 3
#> [9,] 1 3 3 2 3
#> [10,] 1 3 3 2 3
#>
#> $K
#> [1] 2 2 2 2 3 3 3 3 3 3
#>