View source: R/predict_top_k.R
predict_top_k | R Documentation |
Predict the posterior probability, per item, of being ranked among the
top-k
for each assessor. This is useful when the data take the form of
pairwise preferences.
predict_top_k(model_fit, k = 3)
model_fit |
An object of type |
k |
Integer specifying the k in top- |
A dataframe with columns assessor
, item
, and
prob
, where each row states the probability that the given assessor
rates the given item among top-k
.
Other posterior quantities:
assign_cluster()
,
compute_consensus()
,
compute_posterior_intervals()
,
get_acceptance_ratios()
,
heat_plot()
,
plot.BayesMallows()
,
plot.SMCMallows()
,
plot_elbow()
,
plot_top_k()
,
print.BayesMallows()
set.seed(1)
# We use the example dataset with beach preferences. Se the documentation to
# compute_mallows for how to assess the convergence of the algorithm
# We need to save the augmented data, so setting this option to TRUE
model_fit <- compute_mallows(
data = setup_rank_data(preferences = beach_preferences),
compute_options = set_compute_options(
nmc = 1000, burnin = 500, save_aug = TRUE))
# By default, the probability of being top-3 is plotted
# The default plot gives the probability for each assessor
plot_top_k(model_fit)
# We can also plot the probability of being top-5, for each item
plot_top_k(model_fit, k = 5)
# We get the underlying numbers with predict_top_k
probs <- predict_top_k(model_fit)
# To find all items ranked top-3 by assessors 1-3 with probability more than 80 %,
# we do
subset(probs, assessor %in% 1:3 & prob > 0.8)
# We can also plot for clusters
model_fit <- compute_mallows(
data = setup_rank_data(preferences = beach_preferences),
model_options = set_model_options(n_clusters = 3),
compute_options = set_compute_options(
nmc = 1000, burnin = 500, save_aug = TRUE)
)
# The modal ranking in general differs between clusters, but the plot still
# represents the posterior distribution of each user's augmented rankings.
plot_top_k(model_fit)
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