Generate a ranking of the ability based on sampling the posterior distribution of the ranks.

To print this object you should remove the last column PosteriorRank since it contain the whole posterior distribution for each case

get_rank_of_players_df(bpc_object, n = 1000)

Arguments

bpc_object	a bpc object
n	Number of times we will sample the posterior

Value

a data frame. This data frame contains the median of the rank, the mean, the standard deviation of the rank

Examples

# \donttest{
m<-bpc(data = tennis_agresti,
player0 = 'player0',
player1 = 'player1',
result_column = 'y',
model_type = 'bt',
solve_ties = 'none')
#> Running MCMC with 4 parallel chains...
#> 
#> Chain 1 finished in 3.8 seconds.
#> Chain 2 finished in 3.9 seconds.
#> Chain 3 finished in 3.8 seconds.
#> Chain 4 finished in 3.8 seconds.
#> 
#> All 4 chains finished successfully.
#> Mean chain execution time: 3.8 seconds.
#> Total execution time: 4.0 seconds.
rank_m<-get_rank_of_players_df(m,n=100)
print(rank_m)
#>     Parameter MedianRank MeanRank   StdRank
#> 1        Graf          1     1.46 0.6422813
#> 2       Seles          2     2.00 0.9211324
#> 3 Navratilova          3     2.96 0.8518548
#> 4    Sabatini          4     3.82 0.7962031
#> 5     Sanchez          5     4.76 0.5148306
# }