Compute posterior intervals of parameters of interest.
Usage
compute_posterior_intervals(model_fit, ...)
# S3 method for class 'BayesMallows'
compute_posterior_intervals(
model_fit,
parameter = c("alpha", "rho", "cluster_probs"),
level = 0.95,
decimals = 3L,
...
)
# S3 method for class 'SMCMallows'
compute_posterior_intervals(
model_fit,
parameter = c("alpha", "rho"),
level = 0.95,
decimals = 3L,
...
)Arguments
- model_fit
A model object.
- ...
Other arguments. Currently not used.
- parameter
Character string defining which parameter to compute posterior intervals for. One of
"alpha","rho", or"cluster_probs". Default is"alpha".- level
Decimal number in \([0,1]\) specifying the confidence level. Defaults to
0.95.- decimals
Integer specifying the number of decimals to include in posterior intervals and the mean and median. Defaults to
3.
Details
This function computes both the Highest Posterior Density Interval (HPDI), which may be discontinuous for bimodal distributions, and the central posterior interval, which is simply defined by the quantiles of the posterior distribution.
See also
Other posterior quantities:
assign_cluster(),
compute_consensus(),
get_acceptance_ratios(),
heat_plot(),
plot.BayesMallows(),
plot.SMCMallows(),
plot_elbow(),
plot_top_k(),
predict_top_k(),
print.BayesMallows()
Examples
set.seed(1)
model_fit <- compute_mallows(
setup_rank_data(potato_visual),
compute_options = set_compute_options(nmc = 3000, burnin = 1000))
# First we compute the interval for alpha
compute_posterior_intervals(model_fit, parameter = "alpha")
#> parameter mean median hpdi central_interval
#> 1 alpha 10.778 10.818 [9.165,12.566] [8.929,12.351]
# We can reduce the number decimals
compute_posterior_intervals(model_fit, parameter = "alpha", decimals = 2)
#> parameter mean median hpdi central_interval
#> 1 alpha 10.78 10.82 [9.16,12.57] [8.93,12.35]
# By default, we get a 95 % interval. We can change that to 99 %.
compute_posterior_intervals(model_fit, parameter = "alpha", level = 0.99)
#> parameter mean median hpdi central_interval
#> 1 alpha 10.778 10.818 [8.276,12.748] [8.276,12.748]
# We can also compute the posterior interval for the latent ranks rho
compute_posterior_intervals(model_fit, parameter = "rho")
#> parameter item mean median hpdi central_interval
#> 1 rho P1 10 10 [9,12] [9,12]
#> 2 rho P2 17 17 [16,18] [16,18]
#> 3 rho P3 19 19 [19] [19]
#> 4 rho P4 16 16 [16,18] [16,18]
#> 5 rho P5 9 9 [3][9,11] [3,11]
#> 6 rho P6 15 15 [15] [15,16]
#> 7 rho P7 6 6 [5,7] [5,7]
#> 8 rho P8 20 20 [20] [20]
#> 9 rho P9 3 3 [3,4] [3,4]
#> 10 rho P10 4 4 [4,5][7] [3,7]
#> 11 rho P11 11 11 [9,12] [9,12]
#> 12 rho P12 1 1 [1] [1]
#> 13 rho P13 2 2 [2] [2]
#> 14 rho P14 7 7 [6,7] [6,7]
#> 15 rho P15 18 18 [17,18] [17,18]
#> 16 rho P16 8 8 [8,9] [8,9]
#> 17 rho P17 5 5 [4,6][8] [4,8]
#> 18 rho P18 14 14 [13,14] [13,14]
#> 19 rho P19 12 12 [10,12] [10,12]
#> 20 rho P20 13 13 [13,14] [13,14]
if (FALSE) { # \dontrun{
# Posterior intervals of cluster probabilities
model_fit <- compute_mallows(
setup_rank_data(sushi_rankings),
model_options = set_model_options(n_clusters = 5))
burnin(model_fit) <- 1000
compute_posterior_intervals(model_fit, parameter = "alpha")
compute_posterior_intervals(model_fit, parameter = "cluster_probs")
} # }