API

describe_example_data(name) -> String

Return a string containing descriptions of all available datasets.

Examples

julia> describe_example_data("radon") |> println
radon
=====

Radon is a radioactive gas that enters homes through contact points with the ground. It is a carcinogen that is the primary cause of lung cancer in non-smokers. Radon levels vary greatly from household to household.

This example uses an EPA study of radon levels in houses in Minnesota to construct a model with a hierarchy over households within a county. The model includes estimates (gamma) for contextual effects of the uranium per household.

See Gelman and Hill (2006) for details on the example, or https://docs.pymc.io/notebooks/multilevel_modeling.html by Chris Fonnesbeck for details on this implementation.

remote: http://ndownloader.figshare.com/files/24067472

load_example_data(name; kwargs...) -> InferenceObjects.InferenceData
load_example_data() -> Dict{String,AbstractFileMetadata}

Load a local or remote pre-made dataset.

kwargs are forwarded to InferenceObjects.from_netcdf.

Pass no parameters to get a Dict listing all available datasets.

Data files are handled by DataDeps.jl. A file is downloaded only when it is requested and then cached for future use.

Examples

julia> keys(load_example_data())
KeySet for a OrderedCollections.OrderedDict{String, ArviZExampleData.AbstractFileMetadata} with 10 entries. Keys:
  "centered_eight"
  "non_centered_eight"
  "radon"
  "rugby"
  "rugby_field"
  "regression1d"
  "regression10d"
  "classification1d"
  "classification10d"
  "glycan_torsion_angles"

julia> load_example_data("centered_eight")
InferenceData with groups:
  > posterior
  > posterior_predictive
  > log_likelihood
  > sample_stats
  > prior
  > prior_predictive
  > observed_data
  > constant_data